Proceedings IJCSIS Vol 14 Special Issue CIC 2016 Track 2.pdf

Proceedings Track 2 International Conference on Advances in Computational Intelligence in Communication, CIC 2016       19th and 20th October 2016  Pondicherry Engineering College,  Puducherry, India       

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International Journal of Computer Science and Information Security (IJCSIS) Vol. 14 Special Issue CIC 2016 ISSN 1947-5500 © IJCSIS PUBLICATION 2016  Pennsylvania, USA 

     Indexed and technically co‐sponsored by : 

   

 

   

 

   

 

 

 

 

 

   

 

 

 

 

 

 

 

 

 

       

 

Editorial Message from Editorial Board It is our great pleasure to present the CIC 2016 Special Issue (Volume 14 Track 1, 2, 3, 4, 5, 6) of the International Journal of Computer Science and Information Security (IJCSIS). High quality research, survey & review articles are proposed from experts in the field, promoting insight and understanding of the state of the art, and trends in computer science and technology. It especially provides a platform for high-caliber academics, practitioners and PhD/Doctoral graduates to publish completed work and latest research outcomes. According to Google Scholar, up to now papers published in IJCSIS have been cited over 6818 times and the number is quickly increasing. This statistics shows that IJCSIS has established the first step to be an international and prestigious journal in the field of Computer Science and Information Security. There have been many improvements to the processing & indexing of papers; we have also witnessed a significant growth in interest through a higher number of submissions as well as through the breadth and quality of those submissions. IJCSIS is indexed in major academic/scientific databases and important repositories, such as: Google Scholar, Thomson Reuters, ArXiv, CiteSeerX, Cornell’s University Library, Ei Compendex, ISI Scopus, DBLP, DOAJ, ProQuest, ResearchGate, Academia.edu and EBSCO among others. On behalf of IJCSIS community and the sponsors, we congratulate the authors, the reviewers and thank the committees of International Conference On Advances In Computational Intelligence In Communication (CIC 2016) for their outstanding efforts to review and recommend high quality papers for publication. In particular, we would like to thank the international academia and researchers for continued support by citing papers published in IJCSIS. Without their sustained and unselfish commitments, IJCSIS would not have achieved its current premier status. “We support researchers to succeed by providing high visibility & impact value, prestige and excellence in research publication.” For further questions or other suggestions please do not hesitate to contact us at [email protected]. A complete list of journals can be found at: http://sites.google.com/site/ijcsis/ IJCSIS Vol. 14, Special Issue CIC 2016 Edition ISSN 1947-5500 © IJCSIS, USA.

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Open Access This Journal is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source.

Bibliographic Information ISSN: 1947-5500 Monthly publication (Regular Special Issues) Commenced Publication since May 2009

Editorial / Paper Submissions: IJCSIS Managing Editor ([email protected]) Pennsylvania, USA Tel: +1 412 390 5159

INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTATIONAL INTELLIGENCE IN COMMUNICATION CONFERENCE DATE: 19 Oct 2016 to 20 Oct 2016 WEBSITE: http://cic2016.pec.edu/ EMAIL ID: [email protected] DEADLINE FOR PAPER SUBMISSION: 30 Jul 2016 ORGANIZED BY: Dept. of Electronics & Communication Engineering, Pondicherry Engineering College VENUE: Hotel Accord, Puducherry, India CITY: Puducherry, India CONFERENCE KEYWORDS: Computational Intelligence, Wireless communication, Applications and Methodologies ABOUT EVENT: CIC 2016 aims to bring out the contemporary developments and evolving theories,methods and applications of computational intelligence in the design of Mobile and Wireless Communication networks. The main objective of CIC 2016 is to provide a lively forum for the scientific community and industry across the world to present their research findings, explore new directions in computational intelligence, probabilistic and statistical models to solve the ever growing challenges in Wireless Communication. CIC 2016 lays emphasis on computational intelligence techniques such as neural networks, fuzzy systems, evolutionary algorithms, hybrid intelligent systems, uncertain reasoning techniques, and other machine learning methods and how they could be applied for decision making and problem solving in mobile and wireless communication networks. The conference aims to provide an opportunity for researchers to highlight recent developments, share insightful experiences and interactions in the areas coming under the scope of the conference.

INTERNATIONAL EDITORIAL BOARD IJCSIS Editorial Board CIC 2016 Special Issue Guest Editors Dr. Shimon K. Modi [Profile] Dr. P. Dananjayan, Director of Research BSPA Labs, Principal, Pondicherry Engineering College, Purdue University, USA Puduchery, India - Chairperson, CIC 2016 Professor Ying Yang, PhD. [Profile] Dr. Gnanou Florence Sudha, Computer Science Department, Yale University, Professor, Pondicherry Engineering College, USA Publication Chair, CIC 2016 Professor Hamid Reza Naji, PhD. [Profile] Dr. R. Gunasundari, Department of Computer Enigneering, Shahid Professor, Pondicherry Engineering College. Beheshti University, Tehran, Iran Publication Chair, CIC 2016 Professor Yong Li, PhD. [Profile] Dr. Kai Cong [Profile] School of Electronic and Information Intel Corporation, Engineering, Beijing Jiaotong University, & Computer Science Department, Portland State P. R. China University, USA Professor Mokhtar Beldjehem, PhD. [Profile] Dr. Omar A. Alzubi [Profile] Sainte-Anne University, Halifax, NS, Canada Al-Balqa Applied University (BAU), Jordan Professor Yousef Farhaoui, PhD. Dr. Jorge A. Ruiz-Vanoye [Profile] Universidad Autónoma del Estado de Morelos, Mexico Department of Computer Science, Moulay Ismail University, Morocco Dr. Alex Pappachen James [Profile] Prof. Ning Xu, Queensland Micro-nanotechnology center, Wuhan University of Technology, China Griffith University, Australia Dr . Bilal Alatas [Profile] Professor Sanjay Jasola [Profile] Gautam Buddha University Department of Software Engineering, Firat University, Turkey Dr. Siddhivinayak Kulkarni [Profile] Dr. Ioannis V. Koskosas, University of Ballarat, Ballarat, Victoria, University of Western Macedonia, Greece Australia Dr Venu Kuthadi [Profile] Dr. Reza Ebrahimi Atani [Profile] University of Guilan, Iran University of Johannesburg, Johannesburg, RSA Dr. Zhihan lv [Profile] Dr. Dong Zhang [Profile] University of Central Florida, USA Chinese Academy of Science, China Prof. Ghulam Qasim [Profile] Dr. Vahid Esmaeelzadeh [Profile] Iran University of Science and Technology University of Engineering and Technology, Peshawar, Pakistan Prof. Dr. Maqbool Uddin Shaikh [Profile] Dr. Jiliang Zhang [Profile] Northeastern University, China Preston University, Islamabad, Pakistan Dr. Jacek M. Czerniak [Profile] Dr. Musa Peker [Profile] Casimir the Great University in Bydgoszcz, Faculty of Technology, Mugla Sitki Kocman University, Poland Turkey Dr. Wencan Luo [Profile] Dr. Binh P. Nguyen [Profile] National University of Singapore University of Pittsburgh, US Dr. Ijaz Ali Shoukat [Profile] Professor Seifeidne Kadry [Profile] American University of the Middle East, Kuwait King Saud University, Saudi Arabia Dr. Riccardo Colella [Profile] Dr. Yilun Shang [Profile] University of Salento, Italy Tongji University, Shanghai, China Dr. Sachin Kumar [Profile] Dr. Sedat Akleylek [Profile] Ondokuz Mayis University, Turkey Indian Institute of Technology (IIT) Roorkee

Dr Basit Shahzad [Profile] King Saud University, Riyadh - Saudi Arabia Dr. Sherzod Turaev [Profile] International Islamic University Malaysia

ISSN 1947 5500 Copyright © IJCSIS, USA.

Dr Riktesh Srivastava [Profile] Associate Professor, Information Systems, Skyline University College, Sharjah, PO 1797, UAE Dr. Jianguo Ding [Profile] Norwegian University of Science and Technology (NTNU), Norway

 

CIC 2016 Committees  Chief Patron Dr. P.Dananjayan Program Chair

Dr. Xavier Fernando Dr. P.Dananjayan

Principal, Pondicherry Engineering College Director, Ryerson Communications Lab, Ryerson University, Canada. Professor & Dean, Pondicherry Engineering College, India.

General Chair

Dr. M. Tamilarasi

Professor & Head, Dept. of ECE, Pondicherry Engineering College, India

Technical Chairs

Dr. E. Srinivasan

Professor, Pondicherry Engineering College Professor, Pondicherry Engineering College Professor, Pondicherry Engineering College Professor, Pondicherry Engineering College

Dr. K.Vivekanandan Dr. N. Sreenath Dr. G. Nagarajan

Editorial Board

Dr. P. Dananjayan Dr. Gnanou Florence Sudha Dr. R.Gunasundari

 

Professor, Pondicherry Engineering College Professor, Pondicherry Engineering College Professor, Pondicherry Engineering College

CIC 2016 TECHNICAL PROGRAM COMMITTEE Dr. Victor Govindaswamy

Concordia University, Chicago

Dr. Victor Sreeram

University of Western Australia, Australia

Dr. Deepak Selvanathan

Intel, USA

Dr. Jianfei Cai

Nanyang Technological University, Singapore

Prof. Vallavaraj Adhinarayanan

Calledonian College of Engineering, Oman.

Dr. Srinivasan Rajavelu

Oracle, Dubai, UAE

Dr. Suresh Shanmugasundram

Botho University, Gaborone

Dr. Sanjiv Tokekar

Director, IET, DAVV, Indore, India

Dr. Vinayak M.Shed

Government Engineering College, Goa.

Dr. Moinuddin

Jamia Millia Islamia University, New Delhi,

Dr. M.S.Sultaone

College of Engineering ,Pune, India

Dr. D.Sriram Kumar

NIT, Tiruchirrapalli, India

Dr. P.Palanisamy

NIT, Tiruchirrapalli, India

Dr. S.Shanmugavel

Principal, National Engineering College, India

Dr. Maya Ingle

IET, DAVV , Indore, India

Dr. Vaidehi V

IT Dept., MIT, Chennai

Dr. K.Chandrasekaran

NIT Suratkal, India

Dr. K.Gunavathi

PSG tech, Coimbatore, India

Dr. G.Aghila

NIT, Karaikal, India

Dr. Ravibabu Mulaveesala

IIT, Ropar, India

Dr. C.Satish Kumar

RGIT,Kerala, India

Dr. M. Sasikala

Anna University, Chennai

Dr. A.Kavitha

SSN College of Engineering, Chennai.

Dr. A.Rajeswari

CIT,Coimbatore

Dr. A.K. Jayanthy

SRM University.

Dr. B.Surendiran

National Institute of Technology , Karaikal

Dr. C. Lakshmi Deepika

PSG College of Technology,Coimbatore.

Dr.C. Malathy

SRM University, Chennai.

Dr.P.Indumathi

MIT, Anna University,

Dr.S.Malarkkan

MVIT, Puducherry

Dr. Manjesh

Bangalore University, Bangalore

Dr. V. Nagarajan

Adhiparasakthi Engineering College, TN

Dr. L. Nalini Joseph

Anand Institute of Higher Technology, TN

Dr. Rangaiah Leburu

Raja Rajeswari College of Engg. ,Bangalore

Dr. S. Malarvizhi

SRM University,Chennai

Dr.G.F.Ali Ahammed

VTU, Mysuru

Dr. G.K.Rajini

VIT University, Vellore

Dr. J Dhurgadevi

CEG, Anna University, Chennai

Dr.K.Santhi

Guru Nanak Institutions ,Hyderabad.

Dr. K. Venkatalakshmi

UCE, Tindivanam.

Dr. K. Vivekanandan

Pondicherry Engineering College,Puducherry

Dr. N. Sreenath

Pondicherry Engineering College,Puducherry

Dr.S. Kanmani

Pondicherry Engineering College,Puducherry

Dr.M. Ezhilarasan

Pondicherry Engineering College,Puducherry

Dr. S. Saraswathi

Pondicherry Engineering College,Puducherry

Dr. Ka. Selvaradjou

Pondicherry Engineering College,Puducherry

Dr. M. Manikandan

MIT,Anna University, Chennai

Dr. M. A. Bhagyaveni

CEG,Anna University, Chennai

Dr. M.Malleswaran

Anna University, Kanchipuram.

Dr. M. Anburajan

SRM University, Chennai

Dr. J.Martin Leo Manickam

St.Joseph's College of Engineering,Chennai

Dr.Diwakar R . Marur

SRM University

Dr.N. Venkateswaran

SSN,Chennai

Dr.Alamelu Nachiappan

Pondicherry Engineering College,Puducherry

Dr.C.Christober Asir Rajan

Pondicherry Engineering College,Puducherry

Dr.S.Lakshmana Pandian

Pondicherry Engineering College,Puducherry

Dr.K. Saruladha

Pondicherry Engineering College,Puducherry

Dr. P.V. Rao

Raja Rajeswari College of Engg. Bangalore

Dr. P.T. Vanathi

PSG College of Technology,Coimbatore.

Dr. Shanthi Prince

SRM University,Chennai.

Dr.C.Gomathi

SRM University,Chennai.

Dr. A. Lakshmi Devi

SV University College of Engineering, Tirupati

Dr. K. Murugan

Anna University, Chennai

Dr.R.Nakkeeran

SET, Pondicherry University

Dr. T. Shanmuganantham

SET, Pondicherry University

Dr. P. Samundiswary

SET, Pondicherry University

Dr. R. Periyasamy

NIT, Raipur.

Dr.R.Valli

MVIT, Puducherry.

Dr.S.Uma Maheswari

CIT, Coimbatore

Dr.J.Beatrice Seventline

GITAM University, Vishakapatanam.

Dr. S. Siva Sathya

Computer Science Dept. , PU

Dr. T. Chithralekha

Computer Science Dept. , PU

Dr. Ravi Subban

Computer Science Dept. , PU

Dr.T.Shankar

SENSE,VIT University, Vellore

Dr.T.S.Indumathi

VTU, Bangalore

Prof. T. Ramashri

SV University College of Engineering, Tirupati

Dr. V. Jeyalakshmi

CEG, Anna University, Chennai

Dr.V.P. Harigovindan

NIT, Karaikal

Dr. Revathi Venkataraman

SRM University, Chennai

Dr.V.R.Vijaykumar

Anna University, Coimbatore

CIC 2016 Organising chairs Dr. Gnanou Florence Sudha, Pondicherry Engineering College, India Dr. R.Gunasundari, Pondicherry Engineering College, India

CIC 2016 SESSION CHAIRS Dr. V. Jagadeesh Kumar

Professor, IIT Madras, Chennai, India

Dr. Vitawat Sittakul

Professor, King Mongkut's University of Technology, Thailand

Dr. T.G. Palanivelu

Former Principal, PEC & Professor, SMVEC, Puducherry

Dr. V. Prithviraj

Former Principal, PEC & Professor, REC, Chennai

Dr. M.A. Bhagyaveni

Professor, CEG, Anna University, Chennai

Dr. K. Murugan

Professor, CEG, Anna University, Chennai

Dr. V. Saminadan

Professor, Pondicherry Engineering College, Puducherry

Dr. D. Saraswady

Professor, Pondicherry Engineering College, Puducherry

Dr. S. Batmavady

Professor, Pondicherry Engineering College, Puducherry

Dr. K. Kumar

Professor, Pondicherry Engineering College, Puducherry

Dr. G. Sivaradje

Professor, Pondicherry Engineering College, Puducherry

Dr. L.Nithyanandan

Professor, Pondicherry Engineering College, Puducherry

Dr. K.Jayanthi

Professor, Pondicherry Engineering College, Puducherry

Dr. V.Vijayalakshmi

Associate Professor, Pondicherry Engineering College

Dr. S.Tamilselvan

Associate Professor, Pondicherry Engineering College

Dr. M.Thachayani

Assistant Professor, Pondicherry Engineering College

Dr. R. Sandanalakshmi

Assistant Professor, Pondicherry Engineering College

Dr. A.V.Ananthalakshmi

Assistant Professor, Pondicherry Engineering College

List of Papers CIC 2016  TRACK 1 Computational Intelligence in Signal & Image Processing CIC2016_paper 15: Analytical Framework for Identification of Outliers for Unscripted Video Madhu Chandra G, Research Scholar, Dept.of ECE, MS Engineering College, Bangalore, VTU, Belagavi, India Sreerama Reddy G.M, Professor & HOD, Dept. of ECE, CBIT, Kolar, India CIC2016_paper 24: Image Steganography With Huffman Encoding V. Navya, Dept. of ECE, SVEC Tirupati, A.P T. V. S. Gowtham Prasad, Dept. of ECE, SVEC Tirupati, A.P C. Maheswari, Dept. of ECE, SVEC Tirupati, A.P CIC2016_paper 27: Secure Image Transmission Based On Pixel Integration Technique A. D. Senthil Kumar, Department of Instrumentation Engineering, Annamalai University, Chidambaram, India T. S. Anandhi, Department of Instrumentation Engineering, Annamalai University, Chidambaram, India CIC2016_paper 60: Recognition of Gait in Arbitrary Views using Model Free Methods M. Shafiya Banu, Department of Information science and Technology, Anna University, Chennai. M. Sivarathinabala, Department of Information science and Technology, Anna University, Chennai. S. Abirami, Department of Information science and Technology, Anna University, Chennai. CIC2016_paper 67: Study on Watermarking Effect on Different Sub Bands in Joint DWT-DCT based Watermarking Scheme Mohiul Islam, Department of Electronics & Communication Engineering National Institute of Technology Silchar, Assam, India Amarjit Roy, Department of Electronics & Communication Engineering National Institute of Technology Silchar, Assam, India Rabul Hussain Laskar, Department of Electronics & Communication Engineering National Institute of Technology Silchar, Assam, India CIC2016_paper 70: Image Denoising using Hybrid of Bilateral Filter and Histogram Based MultiThresholding With Optimization Technique for WSN H. Rekha, Research Scholar, Department of Electronics engineering, Pondicherry University, Pondicherry, India P. Samundiswary, Assistant Professor, Department of Electronics Engineering, Pondicherry University Pondicherry, India CIC2016_paper 86: Chaos Based Study on Association of Color with Music in the Perspective of Cross-Modal Bias of the Brain Chandrima Roy, Department of Electronics &Communication Engineering, Heritage Institute of Technology, Kolkata, India Souparno Roy (1), Dipak Ghosh (2) (1) Researcher, (2) Professor Emeritus, Sir C.V. Raman Centre for Physics & Music, Kolkata, India CIC2016_paper 99: Estimation of Visual Focus of Attention from Head Orientations in a Single Top-View Image Viswanath K. Reddy, Assistant Professor, Department of Electronic and Communication Engineering, M. S. Ramaiah University of Applied Sciences, Bangalore, India CIC2016_paper 107: Face Recognition Under Varying Blur, Illumination and Expression in an Unconstrained Environment Anubha Pearline.S, M.Tech, Information Technology, Madras Institute of Technology, Chennai, India

Hemalatha.M, Assistant Professor, Information Technology, Madras Institute of Technology, Chennai, India CIC2016_paper 112: Segmentation based Security Enhancement for Medical Images G. Vallathan, Department of Electronics and Communication Engineering, Pondicherry Engineering College, Pondicherry, India. K. Balachandran, Department of Electronics and Communication Engineering, Pondicherry Engineering College, Pondicherry, India. K. Jayanthi, Department of Electronics and Communication Engineering, Pondicherry Engineering College, Pondicherry, India. CIC2016_paper 117: Efficient Stereoscopic 3D Video Transmission over Multiple Network Paths Vishwa kiran S, Thriveni J, Venugopal K R, Dept. Computer Science and Engineering University Visvesvaraya College of Engineering, Bangalore, India Raghuram S, Pushkala Technologies Pvt. Ltd., Bangalore, India CIC2016_paper 130: Speaker Dependent Speech Feature Based Performance Evaluation of Emotional Speech for Indian Native Language Shiva Prasad K M., Research Scholar, Electronics Engg, Jain University, Bengaluru., India G. N. Kodanda Ramaiah, Professor, HOD and Dean R & D, Dept of ECE. K.E.C., Kuppam., India M. B. Manjunatha, Principal, A.I.T., Tumkur.,India CIC2016_paper 131: Formant Frequency Based Analysis of English vowels for various Indian Speakers at different conditions using LPC & default AR modeling Anil Kumar C., Research Scholar, Electronics Engg, Jain University, Bengaluru., India. M. B. Manjunatha, Principal, A.I.T., Tumkur.,India G. N. Kodanda Ramaiah, Professor, HOD and Dean R & D, Dept of ECE. K.E.C., Kuppam., India CIC2016_paper 139: A study of various approaches for enhancement of foggy/hazy images Nandini B.M, Mohanesh B.M The National Institute of Engineering, Mysuru, Karnataka, India. Narasimha Kaulgud, The National Institute of Engineering, Mysuru, Karnataka, India.

 

 

TRACK 2 Computational Intelligence in Wireless Communication Networks CIC2016_paper 17: Design of Cooperative Spectrum Sensing based spectrum access in CR networks using game theory Lavanya Shanmugavel, Fenila Janet, M. A. Bhagyaveni Dept. of ECE, CEG, Anna University, Chennai, INDIA CIC2016_paper 22: An Intelligent Cognitive Radio Receiver for Future Trend Wireless Applications M. Venkata Subbarao, Research Scholar, Department of EE, School of Engineering & Technology, Pondicherry University, Pondicherry, India. P. Samundiswary, Assistant Professor, Department of EE, School of Engineering & Technology, Pondicherry University, Pondicherry, India. CIC2016_paper 31: Vehicular Ad Hoc Networks: New Challenges in Carpooling and Parking Services Amit Kumar Tyagi, Research Scholar, Department of CS&E, Pondicherry Engineering College, Puducherry-605014, India. Sreenath Niladhuri, Professor, Department of CS&E, Pondicherry Engineering College, Puducherry605014, India. CIC2016_paper 36: Efficient Energy Utilisation in Zigbee WDSN using Clustering protocol and RSSI Algorithm Maria Brigitta.R, Department of Electronics Engineering, School of Engineering and Technology Pondicherry University, Puducherry-605 014, India Samundiswary.P, Department of Electronics Engineering, School of Engineering and Technology Pondicherry University, Puducherry-605 014, India CIC2016_paper 37: Prediction of Spacecraft Position by Particle Filter based GPS/INS integrated system Vijayanandh R [1], Raj Kumar G [2] [1], [2] – Assistant Professor, Department of Aeronautical Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India Senthil Kumar M [3], Samyuktha S [4] [3] – Assistant Professor (SRG), [4] – BE Student, Department of Aeronautical Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India CIC2016_paper 68: Dynamic Application Centric Resource Provisioning Algorithm for Wireless Broadband Interworking Network S. Kokila, Department of Electronics and Communication Engineering, Pondicherry Engineering College Puducherry, India G. Sivaradje, Department of Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry, India CIC2016_paper 69: RSSI based Tree Climbing mechanism for dynamic path planning in WSN Thilagavathi P, Research scholar, Department of Information Technology, Jerusalem college of Engineering, Chennai 600100, India Martin Leo Manickam J, Professor, Electronics and Communication Engineering, St. Joseph’s college of Engineering, Chennai 600119, India CIC2016_paper 73: Conductor Backed CPW Fed Slot Antenna for LTE application M. Saranya, S. Robinson,Gulfa Rani Department of ECE, Mount Zion College of Engineering and Technology, Pudukkottai, India CIC2016_paper 74: Power Optimized and Low Noise Tunable BPF using CMOS Active Inductors for RF Applications

A. Narayana Kiran, Assistant Professor, Department of ECE, Shri Vishnu Engineering College for Women, Bhimavaram, India, P. Akhendra Kumar, Research Scholar, Department of ECE, National Institute of Technology, Warangal, Warangal, India CIC2016_paper 76: Butterfly Shaped Microstrip Patch Antenna with Probe Feed for Space Applications Deepanshu Kaushal, PG student, Department of Electronics Engineering, Pondicherry University Pondicherry, India T. Shanmuganatham, Assistant Professor, Department of Electronics Engineering, Pondicherry University, Pondicherry, India CIC2016_paper 78: Primary User Emulation Attack Analysis in Filter Bank Based Spectrum Sensing Cognitive Radio Networks Sabiq P.V. & D. Saraswady Dept. of ECE, Pondicherry Engineering College, Puducherry, India CIC2016_paper 85: Ant Colony Multicast Routing for Delay Tolerant Networks E. Haripriya, Assistant Professor of Computer Science, J.K.K.Nataraja College of Arts & Science, Namakkal, TamilNadu, India K. R. Valluvan, Professor and Head of ECE, Velalar College of Engineering & Technology, Erode, TamilNadu, India CIC2016_paper 87: An Energy-Efficient Key Management Scheme using Trust Model for Wireless Sensor Network P. Raja, Associate Professor, Department of ECE, Sri Manakula Vinayagar Engineering College, Pondicherry, India E. Karthikeyan, Department of ECE, Sri Manakula Vinayagar Engineering College, Pondicherry, India CIC2016_paper 88: Power Efficiency analysis of Four State Markov Model based DRX mechanism with OTSC ratio for Long Term Evolution User Equipment R. Vassoudevan, Research Scholar, Department of Electronics Engineering, Pondicherry University, Puducherry, India P. Samundiswary, Assistant Professor, Department of Electronics Engineering, Pondicherry University Puducherry, India

 

 

TRACK 3 Computational Intelligence in Wireless Communication Networks CIC2016_paper 32: Ensuring Trust and Privacy in Large Carpooling Problems Amit Kumar Tyagi, Research Scholar, Department of CS&E, Pondicherry Engineering College, Puducherry-605014, India. Sreenath Niladhuri, Professor, Department of CS&E, Pondicherry Engineering College, Puducherry605014, India. CIC2016_paper 90: Comparison of Direct Contact Feeding Techniques for Rectangular Microstrip Patch Antenna for X-Band Applications R. Kiruthika, II M.Tech.(ECE), Department of Electronics Engineering, Pondicherry University, Pondicherry Dr. T. Shanmuganantham, Assistant Professor, Department of Electronics Engineering, Pondicherry University, Pondicherry CIC2016_paper 92: New Joint Non Linear Companding and Selective Mapping Method for PAPR Reduction in OFDM System Sandeep Dwivedi, M.Tech. student, Department of Electronics Engineering, School of Engineering and Technology, Pondicherry University, Puducherry-605014 P. Samundiswary, Assistant Professor, Department of Electronics Engineering, School of Engineering and Technology, Pondicherry University, Puducherry-605014 CIC2016_paper 96: Collaborative Location Based Sleep Scheduling With Load Balancing In Sensor-Cloud N. Mahendran, Assistant Professor, Dept of ECE, M. Kumarasamy College of Engineering, Karur, Tamilnadu CIC2016_paper 102: A Review on Routing Protocols of Underwater Wireless Sensor Networks Venkateswarulu Balajivijayan, Assistant Professor, Computer Science and Engineering, Aalim Muhammed Salegh College of Engineering, Chennai,Tamilnadu, India Subbu Neduncheliyan, Professor, Computer Science and Engineering, Jaya College of Engineering and Technology, Chennai, Tamilnadu, India Ramadass Suguna, Professor, Computer Science and Engineering, SKR Engineering College, Chennai, Tamilnadu, India CIC2016_paper 105: A layer based survey on the security issues of cognitive radio networks Tephillah.S, J.Martin Leo Manickam ECE, St.Joseph’s College of Engineering, Chennai, India CIC2016_paper 106: TCOR- Energy Efficient and Power Saving Routing Architecture for Mobile AD HOC Networks S. Sargunavathi Associate professor, ECE, Sriram Engineering College Chennai, India Dr. J.Martin Leo Manickam, Professor, ECE, St Joseph’s Engineering College Chennai-119, India CIC2016_paper 113: Cluster Head Selection in Cognitive Radio Networks using Fruit Fly Algorithm Umadevi K.S., School of Computing Science and Engineering, VIT University, Vellore, India. CIC2016_paper 116: Reputation Based IDS for Gray hole Attack in MANETs K. Ganesh Reddy, K. Radharani, K. V. Sravani, K. Mounika, K. Poojitha, Dept. of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, India P. Santhi Thilagam, Dept. of Computer Science and Engineering, NITK Surathkal, Mangalore, India CIC2016_paper 120: Customer friendly Fast and Dynamic Handover in Heterogeneous Network Environment

T. Senthil Kumar, M. A. Bhagyaveni Department of Electronics and Communication Engineering, College of Engineering, Guindy, Anna University, Chennai, India CIC2016_paper 121: Influence of Road side units on routing information in VANET V. Devarajan, Department of Electronics and Communication Engineering, Pondicherry Engineering College, Pondicherry, India Dr. R. Gunasundari, Department of Electronics and Communication Engineering, Pondicherry Engineering College, Pondicherry, India CIC2016_paper 138: Type-2 Fuzzy based GPS Satellite Selection algorithm for better Geometrical Dilution of Precision Arul Elango G, ECE Department, Pondicherry Engineering College, Puducherry, India Murukesh C and Rajeswari K, EIE Department, Velammal Engineering College, Chennai, India CIC2016_paper 146: Dual Microphone Speech Enhancement Utilizing General Kalman Filter in Mobile Communication Vijay Kiran Battula, Department of ECE, University College of Engineering Vizianagaram, JNTUK, Vizianagaram, INDIA Appala Naidu Gottapu, Department of ECE, University College of Engineering Vizianagaram, JNTUK, Vizianagaram, INDIA CIC2016_paper 147: Artificial Bee Colony Algorithm Based Trustworthy Energy Efficient Routing Protocol D. Sathian, Department of Computer Science, Pondicherry University M. Gunashanthi, Department of Computer Science, Pondicherry University P. Dhavachelvan, Department of Computer Science, Pondicherry University

 

 

TRACK 4 Computational Methods in Biosignal Processing for Telemedicine CIC2016_paper 04: Modified Local Gradient Pattern Based Computation Analysis for the Classification of Mammogram Narain Ponraj (1), Poongodi (2), Merlin Mercy (3) Dept of ECE (1,2) Dept of CSE (3) Karunya University (1) KCE (2) SKCT (3) India CIC2016_paper 09: Study on the use of Multi frequency Bioelectrical Impedance for Classification of Risk of Dengue fever in Indian Children Neelamegam Devarasu (1) and Gnanou Florence Sudha (2) (1,2) Department of Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry, India. CIC2016_paper 14: Automatic Assessment of Non-proliferative Diabetic Retinopathy using Modified ABC Algorithm with Feed Forward Neural Network Vaishnavi J, Ravi Subban, Anousouya M and Punitha Stephen, Department of Computer Science, Pondicherry University, India CIC2016_paper 21: An Effective Liver Cancer Diagnosis through Multi – Temporal Fusion and Decorrelation Stretching Techniques B. Lakshmi Priya, S. Joshi Adaikalamarie, K. Jayanthi Department of ECE, Pondicherry Engineering College, Puducherry CIC2016_paper 26: Detection of Microcalcifications in Digital Mammograms using Fuzzy Euler Graph Segmentation method D. Saraswathi, Department of ECE, Manakula Vinayagar Institute of Technology, Madagadipet, Puducherry, India E. Srinivasan, Department of ECE, Pondicherry Engineering College, Puducherry, India CIC2016_paper 56: Non-Invasive Measurement of Cholesterol Levels Using Eye Image Analysis S.V. Mahesh Kumar (a,*), R. Gunasundari (a) and N. Ezhilvathani (b) (a) Department of Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry, India. (b) Department of Ophthalmology, Indira Gandhi Medical College and Research Institute, Puducherry, India. CIC2016_paper 83: Neural Based Non-Invasive Diagnosis and Classification of Sepsis R. Sandanalakshmi, Rajagopalan.P, AjaiKaran.B, Rajarajan.G Dept. of Electronics and Communication Engg., Pondicherry Engineering College, India CIC2016_paper 93: An Efficient Noise Cancellation Approach suitable for Respiratory Sound Signals Prashanth B.S., Department of Electronics & Communication Engineering, Pondicherry Engineering College, Pillaichavadi, Puducherry, Jayanthi K., Department of Electronics & Communication Engineering, Pondicherry Engineering College, Pillaichavadi, Puducherry, CIC2016_paper 108: Chaotic Cuckoo search and Kapur/Tsallis approach in segmentation of T.Cruzi from blood smear images V. Shanjita Lakshmi, Shiffani. G. Tebby, D. Shriranjani, V. Rajinikanth Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering OMR, Chennai 600 119, Tamil Nadu, India. CIC2016_paper 109: Evaluation of Hypotension using Wavelet and Time Frequency Analysis of Photoplethysmography (PPG) Signal

Remya Raj, Research scholar, Department of ECE, SRM University, Kattankulathur, Chennai, Tamil Nadu-603203, India Dr. J. Selvakumar, Asst. Professor(SG), Department of ECE, SRM University, Kattankulathur, Chennai, Tamil Nadu-603203, India Dr. M. Anburajan, Dept of Biomedical Engineering, SRM University, Kancheepuram, Tamil Nadu, India CIC2016_paper 110: Non-invasive cuffless diagnosis of hypertension using dynamic thermal imaging features Dr. T. Jayanthi, Dept of Biomedical Engineering, SRM University, Kancheepuram, Tamil Nadu, India Dr. M. Anburajan, Dept of Biomedical Engineering, SRM University, Kancheepuram, Tamil Nadu, India CIC2016_paper 134: Classification of abnormal breast neoplasm from mammogram images Angeline SP Kirubha, Anburajan M Biomedical Engineering Department, SRM University, Kattankulathur- 603 203, India CIC2016_paper 140: Detection of Diabetic Retinopathy based on Classification Algorithms Vaishnavi J, Ravi Subban, Anousouya M and Punitha Stephen, Department of Computer Science, Pondicherry University, India CIC2016_paper 33: 3D Ultrasound Imaging For Automated Kidney Stone Detector On FPGA K. Viswanath, Research Scholar, Member IEEE, Department of ECE, Pondicherry Engineering College Pondicherry, India Dr. R. Gunsundari, Professor, Department of ECE, Pondicherry Engineering College, Pondicherry, India

 

 

TRACK 5 Computational Intelligence Methodologies CIC2016_paper 44: Context Aware Web Service Discovery Optimization By Chameleon Inspired Algorithm (1) A. Amirthasaravanan, (2) Paul Rodrigues, (3) R. Sudhesh (1) Department of Information Technology, University College of Engineering, Villupuram, Tamilnadu, India (2) DMI Engineering College, Chennai, Tamilnadu, India (3) Department of Mathematics, BIT Campus, Tiruchirappalli, Tamilnadu, India CIC2016_paper 77: OntoMD: Ontology based Multidimensional Schema Design Approach M. Thenmozhi, Assistant Professor, Dept. of CSE, Pondicherry Engineering College, Puducherry, India P. Ezhilarasi, Assistant Professor, Dept. of CSE, Raak College of Engineering & Technology, Puducherry, India CIC2016_paper 95: Various Computing models in Hadoop Eco System along with the Perspective of Analytics using R and Machine learning Uma Pavan Kumar Kethavarapu, Research Scholar, Department of Computer Science and Engineering, Pondicherry Engineering College Dr. Lakshma Reddy Bhavanam, Principal, BCC College, Bangalore   CIC2016_paper 114: CRNN: CAPTCHA Recognition using Neural Network Umadevi K.S., School of Computing Science and Engineering, VIT University, Vellore, India. Dharmendra Singh Chandel, School of Computing Science and Engineering, VIT University, Vellore, India. CIC2016_paper 115: Using Semantic Fields For Generating Research Paper Summaries A. L. Agasta Adline, Department of Information Technology, Easwari Engineering College, Chennai 600089, TamilNadu, India Harish M, Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai 600025, TamilNadu, India G.S. Mahalakshmi, Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai 600025, TamilNadu, India S. Sendhilkumar, Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai 600025, TamilNadu, India CIC2016_paper 119: Emotion Recognition from Poems by Maximum Posterior Probability Sreeja. P.S, Department of Computer Science, CEG, Anna University, Chennai, India G.S. Mahalakshmi, Department of Computer Science, CEG, Anna University, Chennai, India CIC2016_paper 133: Fast And Enhanced Algorithms For Dynamic Dataset R. Kavitha Kumar, Department of Computer science and Engineering, Pondicherry Engineering College, Pondicherry J. Jayabharathy, Department of Computer science and Engineering, Pondicherry Engineering College, Pondicherry CIC2016_paper 136: Effective OE Position-Wise Mutation Technique for Permutation Encoded Genetic Algorithm to Solve School Bus Routing Problem: mTSP Approach R. Lakshmi, Assistant Professor, Department of Computer Science, Pondicherry University, Puducherry, India CIC2016_paper 142: Two Run Morphological Analysis for POS Tagging of Untagged Words Betina Antony J, Dept of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai 600025, Tamil Nadu, India. G. S. Mahalakshmi, Dept of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai 600025, Tamil Nadu, India

CIC2016_paper 143: Bio-Inspired Schedulers for Public Cloud Environment Vaithianathan Geetha, Department of Information Technology, Pondicherry Engineering College, Puducherry-605014. CIC2016_paper 148: A Scrutiny and Appraisal of Various Optimization Algorithm to Solve MultiObjective Nurse Scheduling Problem M. Rajeswari, Research Scholar, Department of Computer Science, Pondicherry University S. Jaiganesh, Research Scholar, Department of CS, R&D Centre, Bharathiar University, Coimbatore. P. Sujatha, Assistant Professor, Department of Computer Science, Pondicherry University T. Vengattaraman, Assistant Professor, Department of Computer Science, Pondicherry University P. Dhavachelvan, Professor, Department of Computer Science, Pondicherry University,

   

 

TRACK 6 Computational Intelligence Applications CIC2016_paper 2: Information Detection System using 4T Dual Port CAM V. Bharathi, Associate Professor, ECE Department, Sri Manakula Vinayagar Engineering College, Puducherry, India A. Ragasaratha Preethee, ECE Department, Sri Manakula Vinayagar Engineering College, Puducherry, India CIC2016_paper 16: VLSI Implementation of Reverse Converter via Parallel Prefix Adder for Signed Integers (1) P. Rajagopalan , Puducherry – 605 014, India (2) A. V. Ananthalakshmi, Assistant Professor, Pondicherry Engineering College, Puducherry – 605 014, India. CIC2016_paper 63: Optimal Tuning of Coordinated Controller using BBO Algorithm for Stability Enhancement in Power System Gowrishankar Kasilingam (1*), Jagadeesh Pasipuleti (2) (1*) Research Scholar, Department of Electrical Power, Universiti Tenaga Nasional (UNITEN), Malaysia (1*) Associate Professor, Department of ECE, Rajiv Gandhi College of Engg. & Tech.,Pondicherry, INDIA (2) Associate Professor, Department of Electrical Power, Universiti Tenaga Nasional (UNITEN), Malaysia CIC2016_paper 80: Smart Phone Based Speed Breaker Early Warning System Viswanath K. Reddy (1), and Nagesh B. S (2) (1) Assistant Professor in the Department of Electronic and Communication Engineering in M.S. Ramaiah University of Applied Sciences, Bangalore, (2) Robert Bosch Engineering and Services India Pvt.Ltd, Bangalore CIC2016_paper 82: B+ Indexing for Biometric Authentication using Fused Multimodal Biometric Jagadiswary. D, Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry, India Dr. D. Saraswady, Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry, India CIC2016_paper 118: Smart Logistics for Pharmaceutical Industry based on Internet of Things (IoT) M. Pachayappan (1), Nelavala Rajesh (2), G. Saravanan (3) (1) Assistant Professor, Department of International Business, School of Management, Pondicherry University , Puducherry – 605 014, India (2) Assistant Professor, Department of Electronic and Communication, Arunai College of Engineering, Tiruvannamalai – 606601, India (3) Assistant Professor, Department of Electronic and Communication, Valliammai Engineering College, Kattankulathur - 603 203, India CIC2016_paper 123: Determination of Photovoltaic Cell Model Parameters from One-diode Model using Firefly Algorithm coded in Python G. Kanimozhi, Department of Physics, Pondicherry Engineering College, Pondicherry, India R. Rajathy, Department of EEE, Pondicherry Engineering College, Pondicherry, India Harish Kumar, Department of Physics, Pondicherry Engineering College, Pondicherry, India. CIC2016_paper 124: Estimation of maximum power point in photovoltaic cell based on parameters identification approach by Ant Lion Optimizer implemented in IPython G. Kanimozhi, Department of Physics, Pondicherry Engineering College, Pondicherry, India R. Rajathy, Department of EEE, Pondicherry Engineering College, Pondicherry, India

Harish Kumar, Department of Physics, Pondicherry Engineering College, Pondicherry, India. CIC2016_paper 126: Smart Phone Keylogger Detection Technique Using Support Vector Machine S. Geetha, Research Scholar, Dept. of Banking Technology, Pondicherry University G. Shanmugasundaram, Assistant Professor, Department of IT, SMVEC BharathKumar V., UG Students, Department of IT, SMVEC V. Prasanna Venkatesan, Associate Professor, Dept. of Banking Technology, Pondicherry University CIC2016_paper 145: Standby Mode Subthreshold Leakage Power Analysis in Digital Circuits with Variations in Temperature Amuthavalli. G, Department of ECE, Pondicherry Engineering College, Puducherry, India Gunasundari. R, Department of ECE, Pondicherry Engineering College, Puducherry, India

 

       

TRACK 2  Computational Intelligence in  Wireless Communication  Networks           

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Design of Cooperative Spectrum Sensing based spectrum access in CR networks using game theory Lavanya Shanmugavel Dept. of ECE CEG,Anna University Chennai,INDIA [email protected]

Fenila Janet Dept. of ECE CEG,Anna University Chennai,INDIA [email protected]

ABSTRACT-Cognitive Radio (CR) is a promising technology that attempts to solve the inefficient spectrum utilization problem. The most crucial task in cognitive radio is spectrum sensing and access because inaccurate sensing and inappropriate spectrum access by Secondary User (SU) would result in interference to Primary User (PU) typically in overlay CR network. Cooperative techniques which provide better spectrum sensing and access opportunities are employed in Cooperative Cognitive Radio Network (CCRN). Game theory helps in understanding and modeling interactions among users by making use of utility function. Cooperative Spectrum Sensing (CSS) is modeled as hedonic coalition formation game where coalition corresponds to SUs sensing the same channel. CCRN guarantees benefit for both PU and SU by making use of relaying technique. In this work, cooperation between PU and SU is considered. PU chooses a SU to act as relay for its communication with Base Station (BS). For the selection of relay, SU which satisfy distance criteria and probability of detection are selected. PU uses Amplify and Forward (AF) relaying technique and obtains improved transmission rate. As transmission with the help of SU proved advantageous for PU, it in turn rewards access time duration for SU. SU which acted as relay gains more access time and PU could transmit its data in lesser time than its allocated time when relaying techniques are used. Simulation results show that the cooperative spectrum sensing based spectrum access provides better utility than conventional techniques. Moreover, SU is rewarded greater time duration as spectrum access time than the traditional methods. Keywords-Amplify and Forward; Cooperative Cognitive Radio Network (CCRN); Cognitive Radio, game theory; relay, spectrum access; utility

I.

INTRODUCTION

The fixed spectrum allocation policy utilized by FCC has been proved to be very inefficient as stated by studies [1]. Some parts of the spectrum are over-utilized whereas some other parts are under-utilized. Cognitive Radio (CR) aims to resolve this problem of inefficient spectrum utilization [2]. Spectrum access is the chief task in CR networks. The mechanism of spectrum access varies depending on the type of CR network considered. PU and SU coexist in underlay networks where interference is avoided by means of power control. The selection of transmission power for SU is critical as avoidance of interference is the major issue. In overlay networks, SU

M.A.Bhagyaveni Dept.of ECE CEG, Anna University Chennai,INDIA [email protected]

access the spectrum in the absence of PU. Hence, for spectrum access in overlay networks, spectrum sensing is vital to identify the absence of PU. Energy detection is the simplest spectrum sensing mechanism which detects the presence or absence of PU by measuring the energy of the received signal. If the received signal level is greater than the threshold, it signifies the presence of PU. When overlay networks are considered, the spectrum access duration obtained by SU will be very less if PU is present recurrently. This again leads to reduced throughput of SU but interference to PU is almost nil. In underlay networks, though this problem is roughly minimized, proper selection of transmission power is a crucial concern. One more issue is the throughput of PU. If the link with which PU communicates to BS suffers from imperfections, throughput of PU will be affected. Relaying could be used to deal with this trouble. Generally, nodes which are approximately half the distance between source and destination are chosen as relay. If a node (SU) is present between PU and BS, it could be made to act as relay. Relaying will help PU to improve its transmission rate. Usually CR networks are characterized by fewer PUs and greater probability of the link between PU and BS to be poor. Hence, use of relaying in such scenarios is highly appreciated. Moreover, if the position of SUs facilitates them to be used as relay for PU transmission, PU could improve its throughput with SU as relay. The relay node spends its energy for PU transmission. SU in turn would expect some benefit from PU for obvious reasons. In the overlay network, major objective of SU is spectrum access. Therefore, PU rewards some access time duration to the SU. The rest of the paper is organized as follows: Section II describes the related works in literature. Section III explains the proposed work. Section IV presents the results and discussions. Section V concludes this paper with conclusion and future directions. II.

RELATED WORKS

An overview of CR with much weightage to spectrum access and relaying is explained in [3]. It provides an insight to the various technologies used in CR. The most important process which precedes spectrum access is sensing. Yucek et al [4] gives a comprehensive survey of the

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

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Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

various spectrum sensing mechanisms. The advantage as well as the complexity involved in each technique is presented. Cooperative communication is gaining much attention nowadays, due to its numerous advantages. Cooperative Spectrum Sensing (CSS) exploits spatial diversity thereby improving the sensing performance. The performance of sensing is analyzed in terms of probability of detection and false alarm. Conventionally, in centralized CSS one node is used as Fusion Centre (FC) to which all other nodes report their sensing decision. Tradeoff between probability of false alarm and probability of detection in CSS motivated the need for distributed CSS techniques. Analysis of the various CSS techniques is presented in [5]. Beibei Wang et al explores the use of game theory in cognitive radio. Game theory which was formerly used in economics is finding its use in almost all areas from biology to communication. The paper [6] explains the terminologies in game theory and its application in cognitive radio. Distributed CSS could be implemented as coalition formation game [7, 8]. Non-overlapping coalitions of nodes are formed and within each coalition, CSS is performed. The authors have concluded that coalitions formed using game theoretical models have improved performance. A more focused approach to CSS using hedonic coalition game is explained in [9]. The authors have used hedonic coalition formation game to model the coalitions. They have made an attempt to consider sensing accuracy as well as energy consumption in framing utility function. However, only spectrum sensing has been given prime importance in their work. Georgios I. Tsiropoulos et al gives a detailed outline of the existing resource allocation techniques in cognitive radio [10]. All spectrum sharing and spectrum access techniques are discussed in detail. An outstanding contribution to the literature on spectrum access techniques is provided by F. Akyildiz et al [11]. The next generation networks employing cognitive radio techniques and dynamic spectrum access is discussed. Cooperative spectrum access technique is elaborated in [12] where security and relaying are explored for cognitive radio. Relay nodes are selected on the basis of trust and thus the primary work is on security. CSS modeled as hedonic coalition formation game [13] and is implemented in real time using Wireless open Access Research Platform (WARP). From the literature, it is evident that most of the works focus either on sensing or access. Our proposed work captures both these techniques in a game theoretic approach. Our ultimate aim is to enhance the QoS of both PU and SU. This is achieved with the use of relaying. III. PROPOSED WORK A. SYSTEM MODEL The system model considered here comprises of both infrastructure network and an adhoc network. All SUs form the adhoc network. PU and BS constitute the infrastructure network. First, CSS is performed. All SU sense all PU channels initially. The probability of detection is calculated. The PU to which each SU has maximum probability of

detection are sorted out. The SUs which have the maximum probability of detection form coalition. The coalition formation game model considered here is hedonic coalition formation game where the number of coalitions formed will be equal to the number of PU. The decision on the presence or absence of PU is taken by a decision node in each coalition. The decision node combines the sensing result by making use of decision fusion rule namely OR rule. A frame structure for periodic spectrum sensing is considered, where each time frame consists of one sensing subframe and one data transmission subframe. T is used to denote the frame duration. All SUs have the same spectrum sensing duration δ where 0 < δ ≤ T , to denote the spectrum sensing time of the SUs. Therefore, the data transmission duration is (T – δ). SU performs spectrum sensing in the channel of PU and determines the probabilities of detection and false alarm. The probability of detection is the probability of correctly detecting the appearance of PUi under hypothesis H1,i (i.e., a busy channel is determined to be busy correctly). The probability of false alarm is the probability of falsely declaring the appearance of the primary signal under Hypothesis H0,i (i.e., an idle channel is determined to be busy). When energy detection is used as sensing technique, probability of false alarm in channel i by SUj is given by Pf,i,j( , Where

,

=Q((

- 1)

is detection threshold,

)

(1)

is sensing time,

is

noise variance given by = N0B, Q is the complementary distribution function of standard Gaussian, fs is the sensing frequency.Under the hypothesis H1,i, the probability of detection in channel i by STj is given by Pd,i,j( ,

,

where

)=

=

-

– 1)

)

(2)

is the received SNR of PUi

For simplicity , are assumed to be same for all SUs, and so the proability of false alarm remains the same for all SUs. When OR rule is used as decision fusion rule, the total probability of false alarm and total probability of detection is given by (3) and (4) respectively pf,i=1-

(3)

pd,i=1-

(4)

B. SELECTION OF RELAY In each coalition, one node will be selected as relay node. The selection of relay is based on two criteria namely distance and probability of detection.

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Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

(i) The nodes (SU) which are at approximately half the distance between BS and PU are selected. The reason for choosing relay based on distance is that, throughput of the network is maximum [3] under such situation. It can be shown that the best results in terms of minimal overall transmit power can be achieved when the relay is positioned exactly in the middle between the source and the destination; that is, when dS,R = dR,D = dS,D/2

(5)

(ii) If more than one node satisfy (i) criterion, the node which has the maximum probability of detection is selected as relay. Maximum probability of detection implies greater SNR which means the link is reliable.

Do is the penalty for misdetection. Ps is sensing power, Pt is data transmit power, Rcoop is the transmission rate due to Amplify and Forward relaying. Ps|Si|δ is the energy consumed due to sensing and (P0|0,i + P0|1,i )Pt(Tdr) refers to the energy consumed for data transmission. The interpretation of utility function is as: Data transmission takes place when sensing results show that PU is absent. Therefore, only in two scenarios – during probability of detection under hypothesis H0|0,i and during misdetection, this happens . For misdetection, penalty is included to deal with interference to PU. In our work, after PU uses SU as relay for data transmission, PU is absent and hence SU transmits its data. Because of relaying, throughput of PU increases. For AF relaying, transmission rate is given by

C. ACCESS TIME ALLOCATION

(8)

The relaying technique is used to enhance the throughput of the network. For direct transmission between PU and BS, throughput of PU is calculated. Initially, some duration of time is allocated for PU. From the throughput of PU for direct transmission and time allocated, the file size which is transmitted can be determined. Next, as relaying is used, throughput of the PU is measured. The time taken for PU to transmit the same file using relay is calculated. Apparently, PU could transmit the file within lesser time because the capacity of link between relay and PU is greater than the capacity of the direct transmission [3]. The time difference between the allocated time and required time when relaying is used is awarded to SU as spectrum access time. The key steps involved in our proposed work – cooperative spectrum sensing based spectrum access are: coalition formation, relay node selection, Amplify and forward relaying, rewarding access time to SU. D. UTILITY CALCULATION In game theory, utility function is used in analyzing the tradeoff between advantage and disadvantage of the model. In this work, benefit would be the sensing accuracy due to CSS, improvement in transmission rate due to relaying and improved data transmission time duration for SU. The drawback would be energy consumption. On the basis of the above mentioned criteria, utility function for our proposed work is given by: (6) Utility =

(7)

where P(0│0,i) is the probability that absence of PU is correctly detected, |Si| is the number of members in coalition, (Tdr) is data transmission duration which is actually obtained by the excess time duration of PU awarded to SU for access, P(0│1,i) is the probability of misdetection,

where

is the channel gain between Source and

Destination is the channel gain between Source and Relay is the channel gain between Relay and Destination and f(x,y)=xy/(x+y+1) E. ALGORITHM Step 1: Network scenario is created. Step 2: Probability of detection of each PU by each SU is calculated. Step 3: Based on the probability of detection, coalitions of SUs for PUs are identified. Step 4: For each coalition, the nodes at roughly half the distance between PU and BS is selected as possible relay node Step 5: Out of the possible relay nodes selected, node with maximum probability of detection is chosen as relay. Step 6: The ST selected as relay are utilized in cooperative communication for the PU Step 7: Transmission rate is calculated for direct transmission without relaying Step 8: For Amplify and forward relaying, transmission rate is calculated. Step 9: For initial time allocated for PU, file size which is transmitted when direct transmission is used is calculated. Step 10: The time taken to transmit the same file using relaying technique is calculated. Step 11: The time difference between allocated time and required time is noted. Step 12: This time duration is awarded to SU as spectrum access time. Step 13: The improvement in spectrum access time is the reward for SU through cooperative communication. Step 14: Throughput of SU when cooperative technique is employed is compared with non-relaying technique.

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Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Step 15: Average utility is plotted for various values of sensing time and optimum sensing time is obtained. Step 16: Plot of average utility when number of PU increases is also obtained. F. FLOWCHART FOR SELECTION OF RELAY

pair constitute adhoc secondary network. It is assumed that each PU occupies one channel of Bandwidth 10 MHz. The transmit power is taken as 50 mW. The sensing power is taken as 10 mW. As there are five PU’s, 5 coalitions are formed and the SUs belonging to each coalition are shown in Table 1. The channel model between ST-SR link considered here is free space model. The average channel gain is given by the inverse of square of distance. The SUs in each coalition sense the corresponding PU. The nodes which are selected as relay are listed in Table 2. Table 1. Nodes in coalition Coalition for PU SUs in the coalition 1

3,11,16,17,28,30

2

14,29

3 4

2,4,5,8,10,12,15, 21,24,25,26 1,9,13,18,19,20, 23,27

5

6,7,22

Cognitive radio network 100 - 27 - 9

- 6

80

- 14

- 6 - 14 70

- 7

- 22

- 19 - 1

60

- 23 - 23

- 29

-- 13 20 - 18

- 18

- 29 1

- 16 - 16

IV. RESULTS AND DISCUSSIONS A. NETWORK SCENARIO

40

Coalition 1

- 26 - 26

- 17

- 11 - 17

- 11

- 12 Coalition 3

- 28 30

- 3 - 3

- 1

- 28

- 21 - 21 - 15

- 30 Cognitive radio network

10

100 - 27

- 5 - 5

-9

- 27

-9

0

-4

0

10

- 12 - 3

- 8 - 8

- 30

20

-5

-- 13 20

- 19 - 1

Coalition 2

- 7

2

50

90

- 4

Coalition 4

- 2

Coalition 5

Figure 1. Flowchart for relay selection

- 9

- 27

- 5

90

20

30

40

50

- 15 - 25

60

- 24

- 24

- 25

- 4 - 2 70

- 4 80- 2

- 10 90

-2 -6

80

- 14

-6

-- 13 20

- 14 70

- 19- 1

-7 60

- 29 BS

50

- 16 - 16

40

PU 30

-1

-3 -3

- 26 - 26 - 12

- 28 - 28

- 12

-8

- 21

- 30

- 15 -5 -5

10

20

Coalition

-3

- 21

-8

- 30

0

Table 2. Probability of detection and false alarm

- 29 1

10

0

- 18

- 18

- 17

- 11

20

ST - 17

SR

- 11

- 23 - 23

Figure 2. Coalition formation

-- 13 20

- 19 - 1

-7

- 22

30

40

50

- 15 - 25

- 24 - 24

- 25

60

-4 -2 70

-4 80- 2

- 10 90

- 10 100

Figure 2. Cognitive radio network scenario In the cognitive radio network scenario considered, there are 5 PU denoted by dark circles. SU is modeled as ST-SR pair. The dark diamond represents ST. The light colored diamond represents SR. The network is deployed in an area of 100 x 100 meters. The black colored circle at the centre denotes the Base Station. The simulator used is MATLAB. The channel between ST-SR is modeled using free space model for simulations. BS with PU constitute infrastructure primary network while SU modeled as ST-SR

1 2 3 4 5

Probability of detection (Pd) 1 0.9875 1 1 0.995

Probability of false alarm (Pf) 0.0047 0.0016 0.0089 0.0063 0.0023

The ultimate objective of CSS is to reduce the probability of false alarm and increase the probability of detection. The maximum permissible probability of false alarm mentioned in literature is 0.1. From the obtained results in Table 2, it can be seen that the proposed work always has very low probability of false alarm. The probability of detection is always high. Pd of 0.9 indicates that presence of active PU is

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- 10 100

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always detected correctly. Also, the probability of misdetection is always low which signifies minimum interference to PU.

Table 4. Rewarded access time duration for SU Initial time allocated (ms)

Cognitive radio network 100 - 27

-9

- 27

-5

90

-9

-4

10 20 30 40 50

-2 -6

80

- 14

-6 70

Relay

-- 13 20

- 14 - 19- 1

-7

- 16 - 16

40

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- 17

- 11 PU

30

-1

-3 -3

20

- 12 - 28

- 28

- 12

-8

-3

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-8

- 15 -5 -5

0

10

C. PERFORMANCE ANALYSIS   

- 21

- 30 - 30

10

0

- 18

- 18

- 29 1

ST - 17

SR

- 11

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- 29 BS

50

-- 13 20

- 19 - 1

-7

- 22

60

20

30

Time rewarded when SU is used as relay (ms) 61 65 68 71 74

40

50

- 15 - 25

- 24

- 25

60

- 24

-4 -2 70

-4 80- 2

- 10 90

- 10 100

Figure 3. Relay node selection Table 3. Relay node Coalition 1 2 3 4 5

Relay Node 28 14 21 19 7

The link between ST and SR is modeled as free space model. In free space model, the channel gain between ST – SR pair is given by B. ACCESS TIME ALLOCATION CALCULATION EXAMPLE • Available time for data transmission = T • Initial time allocation for SU = tsu • Transmission rate of PU = R bps • For allocated time of tp = (T - tsu) , file size F = transmission rate x time = R x (T - tsu) • Transmission rate of PU using relay = Rcoop Time taken to transmit file after relaying techniques are used = t =

Figure 4. Plot of Average Utility Vs Sensing time In Figure 4, plot of average utility Vs Sensing time is given. As sensing time increases beyond 5 ms, data transmit time reduces. Hence, utility value decreases. If sensing time is less than 5 ms, time available for sensing is less. This may result in inaccurate sensing. For sensing time of 5 ms, maximum utility is obtained. Hence, it is considered as optimum sensing time. The scenario considered for the simulation does not consider relaying technique as sensing time and relaying do not have any direct relation. Figure 5 presents the plot of average utility Vs probability of PU being present. As probability of PU being present increases, the average utility decreases. But, when relaying is used, the average utility is better compared to the conventional techniques which do not use relaying.

• File size of F Mb could be transmitted using relay in t • Remaining time = (Initial time allocated for PU – time utilized to transmit) tresidual = (tp – t) • Gain for SU = (tsu + tresidual )

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9

6

VI.

Average Utility Vs PH1

x 10

5.5

5

A verage utility

4.5

4

3.5

3

Conventional Proposed

2.5

2 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

PH1

Figure 5. Plot of Average utility vs Prob. Of PU being present As the number of PU increases, sensing is better. Also, more number of SUs are used as relays and therefore they achieve greater access time duration. Hence, average utility increases as evident in figure 6. 9

10

Average Utility Vs No of PU

x 10

9

8

7

A v erage utility

6

5

4

3

2 Conventional Proposed

1

0

2

2.5

3

3.5

4

4.5 Number of PU

5

5.5

6

6.5

7

Figure 6. Plot of average utility vs Number of PU V.CONCLUSION Design of cooperative spectrum sensing based spectrum access is discussed. In the cooperative CRN considered in this work, both the primary and secondary network are mutually benefited because of the cooperation. Use of relaying has resulted in faster PU transmission. After PU transmits, the spectrum is free and SU transmits its own data. The time rewarded to SU is greater than the traditional methods. The cooperative CRN discussed in this work always produces improved performance in terms of average utility than conventional techniques. This work could be further extended to include spectrum management and security in cognitive radio network.

REFERENCES

[1] Federal Communications Commission, “Spectrum Policy Task Force Report,” Report ET Docket no. 02-135, Nov. 2002. [2] S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Select. Areas Communication, vol. 23, pp. 201–220, Feb. 2005. [3] Alexander M. Wyglinski, Maziar Nekovee, And Y. Thomas Hou, ‘Cognitive Radio Communications And Networks - Principles And Practice,’ Elsevier Inc, 2010. [4] Yucek T, Arslan H. “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communication Survey Tutorial, pp.116–130, 2009. [5] I. F. Akyildiz, B. F. Lo, and R. Balakrishnan, “Cooperative spectrum sensing in cognitive radio networks: a survey,” Elsevier Physical Communication,vol. 4, no. 1, pp. 40–62, March 2011. [6] Bebei Wang, Yongle Wu, K. J. Ray Liu, “Game theory for cognitive radio networks: An overview,” Elsevier journal on Computer networks, 2010. [7] W. Saad, Z. Han, M. Debbah, A. Hjorungnes, and T. Basar, “Coalitional game theory for communication networks,” IEEE Signal Processing Magazine, vol. 26, no. 5, pp. 77–97, September 2009. [8] W. Saad, Z. Han, T. Basar, M. Debbah, and A. Hjorungnes, “Coalition formation games for collaborative spectrum sensing,” IEEE Trans. Veh.Technol., vol. 60, no. 1, pp. 276–297, January 2011. [9] Xiaolei Hao, Man Hon Cheung, Vincent W. S., “Hedonic Coalition Formation Game for Cooperative Spectrum Sensing and Channel Access in Cognitive Radio Networks”, IEEE Transactions On Wireless Communications, Vol. 11, No. 11, November 2012. [10] Georgios I. Tsiropoulos, Octavia A. Dobre, Mohamed Hossam Ahmed, and Kareem E. Baddour, ‘Radio Resource Allocation Techniques for Efficient Spectrum Access in Cognitive Radio Networks,’ IEEE Communication Surveys & Tutorials, Vol. 18, No. 1, First Quarter, 2016. [11] F. Akyildiz,W.-Y. Lee, M. C. Vuran, and S. Mohanty, ‘NeXt generation/ dynamic spectrum access/cognitive radio wireless networks: A survey,’ Computer Networks, vol. 50, no. 13, pp. 2127–2159, May 2006. [12] Ning Zhang, Nan Cheng, Ning Lu, Haio Zhou, ‘RiskAware Cooperative Spectrum Access for Multi-Channel Cognitive Radio Networks,’ IEEE Journal On Selected Areas In Communications, Vol. 32, No.3, March 2014. [13] Fenila Janet M, Lavanya S, Bhagyaveni M A, ‘Performance Analysis of Cooperative Spectrum Sensing in cognitive radio using game theory’, Proceedings in IEEE Conference on Wireless Communication, Signal Processing and Networking (WISPNET), 2016.

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An Intelligent Cognitive Radio Receiver for Future Trend Wireless Applications M.Venkata Subbarao

P.Samundiswary

Research Scholar, Department of EE, School of Engineering & Technology, Pondicherry University, Pondicherry, India. [email protected]

Assistant Professor, Department of EE, School of Engineering & Technology, Pondicherry University, Pondicherry, India. [email protected]

Abstract— Due to the rapid expansion of wireless communications, more and more spectrum resources are required in future to drive new users. Cognitive Radio (CR) can effectively deal with the growing demand and shortage of the wireless spectrum. To utilize spectrum efficiently, Cognitive Radio permit secondary users to use licensed spectrum bands. Based on the spectrum sensing outcome in cognitive radio, the unlicensed users should change their access strategies to care for the licensed communications. With respect to secondary users the available channel is not fixed. The secondary users send the information in available channel at that particular time. This will cause the transmitter operates at different carrier frequencies and works with different modulation techniques. So in future trend applications the receiver should be an intelligent, because the receiver needs to demodulate whatever the transmitted signal (i.e. for different carrier frequencies and for different modulation techniques). Automatic Modulation Recognition (AMR) at receiver is essential for implementation of cognitive radio. This paper presents a new automatic modulation recognition method for visual detection and classification of various digital modulated signals. Several realistic modulated signals are taken for analysis using reformulated S-Transform (ST) to verify its superiority in Automatic Modulation Recognition. Keywords- Analog and Digital Modulated Signals, Automatic Modulation Recognition (AMR), Time-Frequency Analysis, Wavelet Transform, S Transform (ST).

I.

INTRODUCTION

Automatic Modulation Recognition (AMR) or Automatic Modulation Classification (AMC) is a process performed at the receiver prior to demodulation when the modulation format is unknown to the receiver. Without any awareness of the transmitted data and many indefinite parameters at the receiver, blind recognition is a difficult task. The problem becomes more difficult in real world scenarios particularly in a noisy environment. The ability to automatically choose the correct modulation type used in received signal is a major benefit in a wireless network. Modulation classifiers are usually divided into two categories. The primary category is based on decision-theoretic approach whereas the second category based on pattern recognition [1]. The decision-theoretic method is a probabilistic result based on prior information of probability functions and certain hypotheses [2-4]. The pattern recognition approach is

based on extracting few basic characteristics of the signal called features [5–13]. This approach is a combination of two subsystems: the first one is features extraction subsystem and the second one is classifier subsystem [6]. The second method is more robust and easy to realize if the appropriate features set is considered. The recognition methods extract the signal features which is essential for modulation identification [7-9], higher order cumulants (HOC) [10-13], constellation shape [14], and wavelets transforms [15, 16]. With their efficient performance in pattern recognition problems, a variety of techniques have proposed artificial neural networks (ANNs) as classifiers [5–9]. A new approach for detection of digital modulation type is to use time-frequency transforms like wavelet transform (WT) [17]. Digital modulated signals contain variations in amplitude, frequency or phase. The time-frequency analysis has ability to extract variations in the information and thus allow easy methods to achieve modulation recognition. Lin and Kuo [18] applied Morlet wavelet to detect the phase variations, and they used likelihood task. Based on number of identified phase variations they classified M-ary Phase Shift Keying signal. Ho et al. [19], proposed a technique to recognize Phase Shift Keying and Frequency Shift Keying modulated signals using Haar WT without any extra parameter of a modulated signal. In noiseless environment, the Haar Wavelet Transform magnitude (|HWT|) of a Phase Shift Keying modulated signal is a constant and Frequency Shift Keying signal is a staircase function. Hence the conflict of |HWT| of modulated signal is taken as a feature to differentiate the two signals. Based on the motivations, this paper presents a new automatic modulation recognition method using time frequency analysis called S-Transform. Time–frequency tools have been successfully used in dealing with analysis of nonstationary signals. The ST is an extension of the Short Time Fourier Transform and it allows a signal to be analyzed in time and frequency simultaneously [20-21]. We propose an approach based on time-frequency analysis to estimate variations in amplitude, frequency and phase from the received data for each modulation scheme in the class, based on variations the modulation format is detected. This paper is organized as follows. Section II presents analysis of analog and digital modulated signals using

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a

(1)

⎛ t − b ⎞ Ψ ⎜ ⎟ a ⎠ ⎝

and the mother wavelet Ψ (t ) is an oscillating damped function, "a" represents time dilation and "b" a time translation, 1/√ is an energy normalization factor that keeps the energy of the scaled wavelets the same as the energy of the mother wavelet Ψ (t ) . The discrete version of the CWT transform described in equation (1) is given by ⎛ DWT ( m , n ) = ⎜ ⎜ ⎝

1 a 0m

⎞ ( n − ka ⎟ y ( k )φ ⎟∑ a 0m k ⎠

m 0

)

(2)

Where a and b in (1) are replaced by a0m , and ka0m , k and m take integer values. Actual implementation of the discrete wavelet transform will involve successive pairs of high pass and low pass digital filters at each scaling stage of the transform. In this paper the mother wavelet is chosen as the Daubechies 4 wavelet and only first level decomposition will be adequate in identifying the modulation type. B. Wavelet Packet Transform (WPT) The wavelet packet transform is appropriate to extract the information in high and low frequency bands. WPT is an ideal processing tool of non-stationary time - variable signal.

Amplitude Amplitude

− ∞

1

Frequency

y (t )

30 20 10

Amplitude

∫

100

200

300

400 500 Time Samples Binary ASK Modulated Signal

600

700

800

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300 400 500 600 Time Samples Time Frequency Countour using WT

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800

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400 500 Time Samples Magnitude Response

600

700

800

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600

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800

5 0

0

400 500 Time Samples

Figure 2. Analysis of BASK signal using WT Amplitude

∞

W (a ,b ) =

1 0 -1

0

Input Binary Signal 1 0.5 0

Amplitude

A. Discrete Wavelet Transform(DWT) The Wavelet transform of a time varying signal y(t) is defined in the time- frequency domain as

Input Binary Signal 1 0.5 0

1 0 -1

Frequency

II. ANALYSIS OF MODULATED SIGNALS USING WAVELET TRANSFORM

C. Wavelet Transform Results The discrete wavelet transform and wavelet packet transform are used to analyze different digital modulated signals in a realistic communication network simulated by MATLAB software.

30 20 10

Amplitude

conventional wavelet transform. In Section III, we describe our new time-frequency signal analysis method S-Transform. Simulation results of various analog and digital modulated signals are provided in Section IV and are compared with those with wavelet transform results. Finally, concluding remarks are given in Section V.

0

100

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400 500 Time Samples Binary FSK Modulated Signal

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700

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600

5 0

0

0.1

0.2

0.3

0.4 0.5 0.6 Time Samples

0.7

0.8

0.9

1

Figure 3. Analysis of BFSK signal using WT

Figure 1. Three Level Wavelet Packet Decomposition

After three-level decomposition the signal can be expressed as S = AAA3 + DAA3 + ADA3 + DDA3 + AAD3 + DAD3 + ADD3 + DDD3 Where A is approximated coefficients and D is detailed coefficients.

Figure 2 describes the magnitude response of ASK signal using Wavelet Transform. The time-frequency contour with WT can’t be absolutely localized the variations of signal in time and frequency axis. And the magnitude plot also does not give the useful information regarding variations in the amplitude of the signal. Figure 3 describes the analysis of FSK signal using Wavelet Transform. The plot of frequency versus time in the form of contours does not localize the frequencies and magnitudes clearly. So wavelet transform fails to recognize exact modulation type. From the Figure 2 and Figure 3 it is clear that wavelet transform approach is not suitable for Automatic Modulation Recognition (AMR). So in the next session we present a new Time-Frequency Transform for analysis of digital modulated signals.

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III.

S-TRANSFORM

y(t ) is given by [21]

The S-Transform of a continuous signal − 2iπfτ s(t, f ) = ∫−∞∞ y(τ )w(t − τ , f )e dτ ∞

= y(τ ) ∫ −∞

(3)

− ( t −τ ) 2

2 1 e 2σ ( f ) e − 2iπfτ dτ σ ( f ) 2π

Here σ (f) is the standard deviation of a Gaussian window. For the standard S-transform it is given by (4)

σ ( f ) = 1/ f

Here ‘f’ is fundamental frequency of the time series. For analysis of the signal with different resolutions in our work we consider the standard deviation σ (f) to be (5)

σ ( f ) = k /(a + b | f |)

Here a, b are constants, and the value of k must satisfy the condition k ≤ a 2 + b 2 . Thus, modified Gaussian is extending of the original Gaussian window and is being varied with frequency. The modified version of the window is given by w (t , f ) =

a+b

f

e

2π

k

−

f )2 t2

(a+b

(6)

,k > 0

2k 2

Here t, τ are the time variables and scaling factors k, b can be manage the number of oscillations in the window. By increasing k, the window expands in time domain which leads to increasing the frequency resolution in frequency domain. When b=0 and k=1 we can get the STFT. With modified Gaussian window the generalized Stransform can be rewritten as ∞

S (τ , f ) =

∫ Y (α + f )e

( − 2 π 2α 2 K 2 ) ( a + b

f )

2

(7)

e 2 iπατ d α

−∞

For discrete time signal the discrete version of the STransform can be written as S [ j, n ] =

N −1

∑ Y [m

m =0

+ n ]e

(−2π 2m 2K

2

/( a + b

f

2

)

e

i

2 π mj N

(8)

Where Y[m + n] is shifted in time with the amount of ‘n’ of the Y [m] . Here Y[m] is the DFT of the discrete signal y[k] and is given by Y [ m] =

1 N

N −1

∑ y[k ]e

−j

2πmk N

(9)

Figure 4. Flow Chart for Modulation Recognition using ST

IV.

SIMULATION RESULTS & DISCUSSIONS

In our work we have discussed various types of analog and digital modulated signals such as AM, FM, Binary ASK, FSK, PSK and 4-ASK, 4-FSK, 8-ASK, 8-FSK Signals and their corresponding magnitude vs. time are analyzed with MATLAB software. The ST output shows the plot of the normalized frequency contour versus time, and amplitude contour of the signal versus time. Figures 5-13 show the time-frequency contours of some typical analog and digital modulated signals with ST, and these contours clearly represents the amplitude, frequency and phase variations occurred in the modulated signal. Figure 5 (b) shows the Binary ASK modulated signal. In Figure 5(c) the normalized time-frequency contour from ST is shown. Figure 5(d) gives the magnitude-time spectrum obtained by ST. The magnitude response clearly represents the variations in the amplitude and these variations are discrete.

k =0

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Input Binary Signal

0

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400 500 600 Time Samples Binary ASK Modulated Signal

400 500 Time Samples ST Time Frequency Countour

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300

400 500 600 Time Samples ST Time Frequency Countour

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400 500 Time Samples ST Time Frequency Countour

400 500 Time Samples ST Magnitude Response

300

400 500 Time Samples

Figure 8. Analysis of 4-ASK using ST

Input Binary Signal

1 0 -1

400 500 Time Samples QASK Modulated Signal

0.5

Input Binary Signal 1 0.5 0

0

100

200

300 400 500 600 700 -----> Time Samples QFSK Modulated Signal

800

0

100

200

300 400 500 600 700 -----> Time Samples ST Time Frequency Countour

800

0

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300 400 500 600 700 -----> Time Samples ST Magnitude Response

0

100

200

Figure 6. Analysis of BFSK using ST

1 0.5 0

300

700

Amplitude

0

Input Binary Signal 1 0.5 0

Am plitude

1 0 -1

0

0.5

A m plitude

Norm alis ed Freq.

A m plitude

A m plitude

Figure 5. Analysis of BASK using ST

1 0.5 0

Figure 7 (b) shows the Binary PSK modulated signal. Figure 7 (c) show the time-frequency contour obtained from ST analysis. From Figures 7 (c) & (d) it is clear that for a Phase Shift Keying modulated signals there is no variations in normalized frequency and magnitude but whenever phase shift occur in modulated signal, but it can be observed that there is a spike in time-frequency contour. A m plitude

200

A m plitude

100

0.5

Amplitude

Normalised Freq.

1 0 -1

0

Figure 6 (b) shows the Binary FSK modulated signal. Figures 6 (c) & (d) show similar plots as in Figure 5 obtained from ST analysis. From the time-frequency contour in Figure 6 it shows the signal having two frequency components and there are no variations in magnitude response. So the signal is consider as a digital binary frequency shift keying signal.

Norm alised Freq.

1 0.5 0

Amplitude

Amplitude

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

1 0 -1

0.5 0

1 0 -1

300

400

500 600 700 -----> Time Samples

700

Figure 9. Analysis of 4-FSK using ST

600

700

Figures 8-11 are similar plots as in Figures 5-7 obtained from ST analysis. From Figure 8(d) the magnitude response consists of four different magnitude levels so it can be classified as 4-ASK.

Figure 7. Analysis of BPSK using ST

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20 0 -20

0

500

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1500 2000 Time ST Time Frequency Countour

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Normalised Freq.

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1.5 1 0.5

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400

0.5 0

2500

Input Baseband Signal 1 0 -1

Amplitude

A m plitude

A m plitude Norm alis ed Freq. A m plitude

1 0 -1

500

2000

Figure 12. Analysis of AM using ST

Input Binary Signal

0

1500 Time

2000

Figure 10. Analysis of 8-ASK using ST

1 0.5 0

1500 2000 Time ST Magnitude Response

20

Amplitude

0

500

Amplitude

50 0 -50

0

3000

0.5

Amplitude

Amplitude Amplitude

Input Binary Signal 1 0.5 0

Normalised Freq.

Amplitude Modulated Signal Amplitude

From Figure 9(c) the time-frequency contour represents 4 levels so it can be recognized as 4-FSK. Similarly the Figure 10 and Figure 11 can be recognized as 8-ASK and 8-FSK modulated signals.

600

700

Figure 13. Analysis of FM using ST

2000

2000

500 Time

2500

If SNR is more than 15dB, we observe that all modulations are accurately identified, i.e. with 100% of accuracy. At SNR is 10dB there is a small reduction of detection accuracy in the FSK cases. But overall the performance of the ST based modulation identification is better than the earlier methods. TABLE I.

Figure 11. Analysis of 8-FSK using ST

Figure 12 shows the analysis of continuous AM signal using S-Transform. The magnitude response gives the continuous variations in amplitude. So the signal is classified as AM signal. Figure 13 represents the analysis of continuous FM signal using S-Transform. The time-frequency contour gives the continuous variations in frequency but the magnitude is constant therefore the signal is classified as FM signal. The time-frequency contours of the ST output shows a decrease or increase in magnitude for ASK, AM and variations in frequency for FSK, FM and frequency spikes for PSK which present a superior visual recognition in contrast to the wavelet transform. The following tables show the performance of ST under different Noise conditions.

PERCENTAGE OF DETECTION ACCURACY

S. No.

Modulation Type

Detection Accuracy (%) At SNR=15dB

Detection Accuracy (%) At SNR= 10dB

1 2 3 4 5 6 7 8 9

AM FM 2ASK 4ASK 8ASK 2FSK 4FSK 8FSK BPSK

100 100 100 100 100 100 100 100 100

100 100 100 100 100 99.5 99 98 98

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Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

V.

CONCLUSSION

A new approach for automatic modulation recognition of analog and digital modulated signals in a communication network is presented in this paper. The S-Transform (ST) with a variable window (Gaussian window) as a function of modulated signal frequency is used to produce TimeFrequency contours which were superior localized than Wavelet Transform. Automatic recognition can be done by extracting feature vectors from the Time-Frequency contours of S-Transform and passing those relevant feature vectors through an intelligent classifier for pattern recognition. REFERENCES [1]

OA Dobre, A Abdi, Y Bar-Ness, W Su, Survey of automatic modulation classification techniques: classical approaches and new trends. IET Communications 1(2), 137–156 (2007). [2] W Wei, JM Mendel, Maximum-likelihood classification for digital amplitude-phase modulations. IEEE Transactions on Communications 48(2), 189–193 (2000). [3] V. Choqueuse, S. Azou, K. Yao, L. Collin, and G. Burel, “Blind modulation recognition for MIMO systems,” MTA Rev., vol. 19, no. 2, pp. 183–196, Jun. 2009. [4] OA Dobre, F Hameed, Likelihood-based algorithms for linear digital modulation classification in fading CHANNELS. Proceedings of the Canadian Conference on Electrical and Computer Engineering (CCECE '06), 2006, Ottawa, Canada, 1347–1350 [5] A Ebrahimzadeh, A Ranjbar, Intelligent digital signal-type identification. Engineering Applications of Artificial Intelligence 21(4), 569–577 (2008). [6] Y Zhao, G Ren, Z Zhong, Modulation recognition of SDR receivers based on WNN. Proceedings of the 63rd IEEE Vehicular Technology Conference (VTC '06), May 2006, 2140–2143. [7] MLD Wong, AK Nandi, Automatic digital modulation recognition using artificial neural network and genetic algorithm. Signal Processing 84(2), 351–365 (2004). [8] K. Hassan, I. Dayoub,W. Hamouda, C. Nzeza, and M. Berbineau, “Blind Digital Modulation Identification for Spatially-Correlated MIMO systems,” IEEE Trans. Wireless Commun., vol. 11, no. 2, pp. 683–693, Feb. 2012. [9] AK Nandi, EE Azzouz, Algorithms for automatic modulation recognition of communication signals. IEEE Transactions on Communications 46(4), 431–436 (1998). [10] A Swami, BM Sadler, Hierarchical digital modulation classification using cumulants. IEEE Transactions on Communications 48(3), 416– 429 (2000). [11] OA Dobre, Y Bar-Ness, W Su, Robust QAM modulation classification algorithm using cyclic cumulants. Proceedings of the IEEE Wireless Communications and Networking Conference - 2004, 745–748. [12] M. Muhlhaus, M. Oner, O. Dobre, H. Jakel, and F. Jondral, “A novel algorithm for MIMO signal classification using higher-order cumulants,” in Proc. IEEE Radio Wireless Symp. (RWS), Jan. 2013, pp. 7–9.

[13] M. Muhlhaus, M. Oner, O. Dobre, and F. Jondral, “A low complexity modulation classification algorithm for MIMO systems,” IEEE Commun. Lett., vol. 17, no. 10, pp. 1881–1884, Oct. 2013. [14] BG Mobasseri, Digital modulation classification using constellation shape. Signal Processing 80(2), 251–277 (2000). [15] P Prakasam, M Madheswaran, Modulation identification algorithm for adaptive demodulator in software defined radios using wavelet transform. International Journal of Signal Processing 5(1), 74–81 (2009) [16] K Maliatsos, S Vassaki, P Constantinou, Interclass and intraclass modulation recognition using the wavelet transform. Proceedings of the 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '07), September 2007. [17] Y. T. Chan, Wavelet Basics, Kluwer Academic Publishers, 1995. [18] Y.C. Lin and C.-C. Jay Kuo, “Modulation classification using wavelet transform,” in Proceedings SPIE, vol. 2303, pp. 260-271. [19] K.C. Ho, W. Prokopiw and Y.T. Chan, “Identification of M-ary PSK and FSK signals by the wavelet transform,” in Proceedings IEEE Military Communications Conf., pp. 886-890, California, November 1995. [20] M. Venkata Subbarao, N.Sayedu Khasim, Jagadeesh Thati and M.H.H.Sastry, “ A Novel Technique for Time-Frequency Analysis of Digitally Modulated Signals”, International Journal of Signal Processing, Image Processing and Pattern Recognition(IJSIP), Volume 6, Issue 2, pp. 1-14, April 2013. [21] M. Venkata Subbarao, N.Sayedu Khasim and Jagadeesh Thati, “Analysis of Non-Stationary Power Quality Signals” Elixir Online Journal on Electrical Engineering, Vol 53, pp. 11815-11818, December 2012. AUTHORS PROFILE M.Venkata Subbarao received his B.Tech degree in Electronics and Communication Engineering from JNTU Hyderabad, India in 2008 and M.Tech degree in Digital Electronics and Communication Systems from JNTU Kakinada, India in 2011. Presently he is pursuing Ph.D. at Pondicherry University, India. He is currently working as Assistant Professor in the Department of ECE, Shri Vishnu Engineering College for Women (Autonomous), Bhimavaram, A.P, India. He published more than 20 papers in various National and International Conferences and Journals. His are of interest includes Advanced Communication systems, Cognitive Radio, Signal Processing and Pattern Recognition. P. Samundiswary received her B.Tech degree and M.Tech degree in Electronics and Communication Engineering from Pondicherry Engineering College affiliated to Pondicherry University, Pondicherry, India in 1997 and 2003 respectively. She received her Ph. D degree from Pondicherry Engineering College affiliated to Pondicherry University, Pondicherry, India in 2011. She has been working in teaching profession since 1998. Presently, she is working as Assistant Professor in the Department of Electronics Engineering, School of Engineering and Technology, Pondicherry Central University, India. She has nearly 18 years of teaching experience. She has published more than 70 papers in national and international conference proceedings and journals. She has co-authored a chapter of the book published by INTECH Publishers. She has been one of the authors of the book published by LAMBERT Academic Publishing. Her area of interest includes Wireless Communication and Networks, Wireless Security and Computer Networks. She will be available at [email protected]

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

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Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Vehicular Ad Hoc Networks: New Challenges in Carpooling and Parking Services Amit Kumar Tyagi

Sreenath Niladhuri

Research Scholar Department of CS&E, Pondicherry Engineering College, Puducherry-605014, India. [email protected]

Professor Department of CS&E, Pondicherry Engineering College, Puducherry-605014, India. [email protected]

Abstract- Now a days, a new kind of ad hoc network is hitting the streets/roads called Vehicular Ad hoc Networks (VANETs). In ad hoc networks, vehicles communicate with each other and possibly with a roadside infrastructure for example Road Side Unit (RSU), Certified Authority (CA), etc. to provide a long list of applications varying from transit safety to driver assistance and Internet access. In case of using Global positioning system (GPS) and RSU, vehicle driver used Social networking (internet technologies) to park their vehicle in a safe and secure area and with this, provide carpooling services to multiple users form similar or different locations. Around the world, people mostly prefer public transportation to move around places. But when oil prices are decreasing, most of the people prefer riding via their own vehicles. Which increases the total number of vehicles on road, also leads to several problems like, congestion, environmental degradation, parking and energy problems. On other side, the industrialization of the world, increase in population, slow paced city development and mismanagement of the available parking space has resulted in parking related problems. This work mainly focuses on carpooling and parking problems to reduce the number of vehicles on road. Different approaches and techniques to solve these issues emerged which address fields of emission reduction, increase efficiency of vehicle energy alternative, decrease road density with care of safety and comfort, etc. This work discusses various emerging drifts and elaborates on one of the ways to reduce the vehicular density and emission. During this work, we have identified carpooling as one such solution to provide user, flexibility in time, enjoyable, efficient and safe and secure ride. Also this paper will be able to reduce the problems which are arising due to unavailability of a reliable, efficient parking space. Keywords: Vehicular Ad hoc Networks; Location Privacy; Trust; Carpooling; Parking; Benefits; Challenges. I. INTRODUCTION A number of interesting and desired applications of Intelligent Transportation Systems (ITS) have been stimulating the development of a new kind of ad hoc network i.e. Vehicular Ad hoc Networks (VANETs). In these networks, vehicles are equipped with communication equipment [1] that allows them to exchange messages with each other in Vehicle-to-Vehicle communication (V2V) and also to exchange messages with a roadside network infrastructure (Vehicle-to-Roadside Communication – V2R). A number of applications are envisioned for these networks. Some of which are already possible in some recently designed vehicles are vehicle collision warning; security distance warning; driver assistance; cooperative driving; cooperative sharing, cooperative cruise control; dissemination of road information; Internet access; map location; automatic

parking and driverless vehicles. Accessing the physical location of a vehicle driver/passenger using GPS or any other device/method breach the privacy and trust barriers between driver and passengers (during carpooling), between car owner and service provider (during parking). Several researchers find out various types of Cyber Transportation Systems (CTS) which are: • On-Road Advertisement Scheduling and Delivery • New Taxi Dispatch and Rideshare Services i.e. Taxi Dispatch with Cruising Guidance and Rideshare System with Transfer-Allowed • Streaming Video Delivery Using Heterogeneous Networks • Notify jamming problem or traffic management problem/ An automated navigated system • Parking availability information system This work discusses about the localization requirements of a number of VANET’s applications (for example, ridesharing system, parking availability system etc.). Carpooling (or ridesharing or pool’ up) is a scheme according to which several people share a common vehicle simultaneously, in order to reach common, or nearby destinations. After reaching at final destination, every vehicle user needs parking to park his vehicle. Parking systems help drivers to find vacant parking spaces when they are already on the network. The carpooling problem has been approached from diverse points of view [2] i.e. “how to match between people to share a ride”, and to decide “who picks up whom with their vehicle”? So that it will be worthwhile (a challenging task) both for the driver and all the passengers. Similarly in parking, “How to find perfect safe, secure and privacy persevered area” to park their vehicles also a challenging task for vehicle users. In carpooling, even if we assume that every driver can pick up at least one (but maximum four, in case of female passenger, it can be max. one) passenger. Again finding total passengers during pool up is NP problem. Because we do not know much about passengers during pool up i.e. “who he is and what about his character etc.”? Some people want to travel with their age’s passenger or habits or areas/street only. Nonsmoker does not want to travel with smokers or female passengers do not prefer traveling with young age male persons. Similarly, no user wants to park his vehicle just like that i.e. without security in a street/market. Similarly finding a secured and privacy preserved parking area for small amount of time (for 1 or 2 hours) faced several challenges. Here parking situation can be arises into two ways: • A person who is driving his own vehicle and finding a parking space • A driver who is carrying or providing ridesharing to several customers and finding a parking space after get down of all passengers For above both issues, trust and privacy matter but in different way like for a single person trust is no more required during his

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

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Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

journey, but it matters in cases of many people. For these issues, we need to be trusted to others, to provide privacy preserved ridesharing or parking to moving users. Trust is “a peer’s belief in another peer’s capabilities, honesty and reliability based on its own direct experiences”. A detailed description is given in [28] about maintain trust among peers (neighbor users). A new approach to establish trust based on authentication (among peers) is proposed in [30]. As discussed, during parking time, trust is an essential element between service provider and owner of the vehicle while in carpooling, trust and privacy issues required (between passengers and drivers) to make their journey/riding experiences awesome. With carpooling, we create some of benefits which are (for more details, refer section 3): • Portable: As it is a mobile application, portability is one of the most noticeable benefit of Pool’ up. Mobiles are handy and can be carried anywhere easily. • Real time: This application provides real time data about the users interested in carpooling and their location. • Flexibility: This application notifies users in case a participant in running late. It enables users to continue their work in case their fellow user is not able to reach on time. • Low cost: As it runs on mobile, it requires low cost and maintenance. All that is maintenance required is a cell phone with GPRS connection. • Easy to use: The only job of the user is to fill in some information about the source and destination of his journey and he will receive the relevant data transferred to his cell phone in an understandable manner. Further some enabling technologies (for example, Smart Phones; User-Friendly Interfaces - iPhone, Android (Apps); Constant Data Network; Connections; GPS-Functionality; Ride Matching & Routing Algorithm; Database.) and features (for example, Social Network Integration (Facebook, Institutions, twitter); Stored User Profiles; Rideshare User Evaluation (eBay); Automated Financial Transactions; Incentives/Loyalty; Rewards) are available to increase carpooling services on road. Hence we need such system in parking and carpooling services which can help the economic, social, and safety based aspects to the security i.e. it preserve the environment (reduce greenhouses or CO2 gases) via saving fuel, time and total number of vehicle over road. Among the main contributions of this paper are: • An extensive review of variants of the wellestablished mechanism in carpooling and parking services. • Discussion of various challenges according to different-different situations in respective services (i.e. in VANET’s applications). • A road ahead for future work Finally this work is organized as; Section 2 discusses about the literature review regarding VANET applications. Section 3 explains motivation behind this work including functional and non-functional entities regarding to respective approaches. Further section 4 discusses about challenges in respective approaches. Section 5 provides future directions for carpooling and parking schemes. Finally Section 6 concludes this works in brief. In this work, ‘carpooling’ word is interchangeably used with word ‘ridesharing’ or ‘pool’ up’. II. Literature Review In last few years, vehicular networks (or moving objects) are gaining more and more attraction from the researchers and the

automobile industries. Vehicular Ad hoc Networks are networks of communication between vehicles and roadside units. Due to enhancement in vehicle technology and increases in people’s salaries or reduction in fuel prices, every day a significant percentage of human beings have their own vehicles that are travelling in single occupancy vehicles and searching for a secure parking space over road/street network. Less number of vehicles does not create any problem, but when more vehicles come out at peak hours. At that time, we need some reliable and efficient solutions to reduce total number of vehicles using VANET applications (carpooling, finding parking), also to reduce congestion, fuel and pollution problems. Carpooling and Parking is an effective means of reducing traffic, fuel, time and cost. This work need to discuss about VANETs features, attacks etc., because today’s vehicles have an important role human beings life (via providing reliable services to public users). 2.1 Carpooling Services As discussed, using a private vehicle is a transportation system very common in industrialized countries. However, it causes different problems such as overuse of oil, traffic jams causing earth pollution, health problems [3] and an inefficient use of personal time. One possible solution to overcome these problems is carpooling, i.e. sharing a trip on a private car of a driver with one or more passengers (but not more than a limit, i.e. min. 1 and max. 4). In carpooling, in case of minimum passengers, maximum privacy is achieved but in case of maximum no. of passengers, privacy can be preserved but not guaranteed. Carpooling would reduce the number of cars on road/streets, hence providing worldwide environmental, economic and social benefits. The matching of drivers and passengers can be facilitated by information and communication technologies. Typically drivers insert on a web-site the availability of empty seats on his/her car for a planned trip and potential passengers can search for trips and contact concern drivers. This process is slow and can be appropriate for long trips i.e. planned trips in advance [3]. We call this Static carpooling and we note that it is not used frequently by people. There are already several websites offering this service and in fact the only real open challenge is widespread adoption. Dynamic carpooling provides riding to passengers who have booked/need riding in urgent over road/street networks. Dynamic carpooling takes advantage of the recent and increasing adoption of Internet-connected geo-aware mobile devices for enabling /improving trip opportunities. Passengers should be accessible ridesharing scheme nearby the requested street for a suitable ride [4]. Currently, there are no dynamic carpooling systems widely used. Every attempt to create and organize such systems failed. This paper reviews the state of the art of dynamic carpooling. It identifies the essential issues against the adoption of dynamic carpooling systems. The objective of the carpool matching and daily commute processes is to minimize the cost (for example, route length, commute time, or some combined cost) of the daily commute. Decreasing rate in oil prices, improve transportation facilities and increasing incomes are potential reasons for this decreases in carpool trips i.e. using own vehicle for everywhere decrease carpool services. In result, it creates several problems for environment/nature and human beings. Additionally, when there are several vehicle drivers/public transport are on road, then they need enough space to park their vehicle also in a safe and secure location. This problem is discussed in section 2.2. Hence there is a dire need for

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

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Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

a secure, intelligent, trusted, privacy preserved carpooling scheme which can be used to provide trusted ride to passengers. 2.1.1 History and the Current State This work concluded that the cost of time and convenience are the two decisive factors [5] in the decision to using carpool/pool ‘up. Articles [6, 7] provide extensive reviews of the history of carpooling activity in the United States. Since the world oil shortages in the mid-1970s, Federal and local governments have implemented a variety of policies and programs to encourage carpooling activity. After that period, carpooling became increasingly popular in United States, with nearly 20% of commute trips using a carpool in the 1970s. However, this decreased precipitously after 1980, increase approximately 12% in 2007 and again has dropped to approximately 10% in 2015, due to low prices of oil. Now days, oil prices are going down daily all over the world, which creates a worst situation for future comers. For example, in Delhi, Shanghai etc., pollution is reaching at its worst level. Living in such places is killing yourself. Several companies are selling oxygen in bottles with high prices. Besides that, several companies have market of crores about air purifier implantation. Ruining of environment is total number of vehicles increased over road. It is due to increasing in people’s salaries, increases in necessity of people (according to their requirements), reduction in cost of vehicles and reduction in fuel prices. Which is a worrying concern regarding about to protect this nature? To encourage carpooling, many transportation agencies have built extensive networks of High-Occupancy Vehicle (HOV) lanes. However, the efficiency of and benefits from HOV lanes remain topics of controversy [8]. In [9] the author investigated the influence of carpool lanes on overall transportation network performance. That research found that, using actual trip data, the presence of a reserved carpool lane on a congested highway can increase commute time. Similarly, [10] have found that the overall efficiency of a highway decreases with the presence of an HOV lane. It has been observed that HOV lanes frequently carry fewer people than general purpose lanes. In paper [11], the author demonstrated that a high proportion of two-person family carpools, also known as “fampools”, dramatically reduced the expected benefit of HOV lanes that were built with the expectation of carpools (or vanpools) carrying limited persons per vehicle. This finding provides a primary motivation for the research presented here. If efficient means of generating larger carpools can be implemented, the expected benefits of HOV lanes can be more easily realized. 2.1.2 Influencing Factors In the process of designing a carpooling matching service, it is critical to understand the factors involved in carpooling activities; including people’s perceptions of the benefits and costs of carpooling, and their concerns regarding safety [5]. A survey on a total of 996 respondents, conducted in Lisbon, Portugal cities which shows that the poor carpooling schedule and trust level between strangers are two major obstructions for carpool activities. Location and schedule requirements seriously limit the convenience and flexibility of carpools [5]. Although a large proportion of commuters likely share a similar commute route and schedule (thus leading to rush hour traffic), it is difficult for them to find each other and coordinate their travel. Therefore, an efficient carpool matching service which enables commuters to develop a potential carpool team can be a critical element in encouraging carpool use. Hence following factors are important factors to make mobile dynamic taxi sharing system successful:

•

Taxi driver compensation: premium fares or a ticketing system, which gives the taxi driver compensation for participating in a shared taxi program beyond the standard metered rate. • Participant cost minimization: organization of shared taxi such that travel time is minimized and a maximum of three people are in each taxi to minimize selfishness costs. • Participant and taxi driver safety maximization: verify personal information of passengers and drivers, include a safety device (such as a technology that allows for real time 911 communication) in the taxi, only conduct taxi share passenger matching between passengers who are members of a common group. • Publicity: through advertisements, governmental assistance or shared screens. Shared screens would be larger platforms in a conference, business or university setting where potential users could see taxi trips currently available and sign up at the shared screen or using their personal device. With suitable matching algorithms and a culturally receptive passenger and driver base, these four factors should create a viable taxi share system. 2.1.3 Matching Models and Services Numerous carpooling services have been developed with a wide range of approaches and functions. This work shows that ridesharing services are loosely which can be grouped into four categories [5]: 1) services that list information but provide no explicit matching, 2) hardware- and communications-focused approaches, 3) services that use spatial information but not network-based information, and 4) services that use networkbased spatial information to match users and provide good carpool routes. Now for real word examples, maximum communication in ridesharing is done by human beings to share a cab /transport vehicle using his cell phone/internet network technologies. And this communication is provided by trusted third party. Vehicular nodes and cloud infrastructure [12] cooperate with each other to provide such services (with services such as traffic information, timely warning messages, and infotainment etc.) to VANET users. “BlueNet” is a carpool service in Taiwan with a mobile client and cloud-based carpool matching module. The system adopted a genetic algorithm approach in order to provide network-based carpool matching. In [13], author conducted a carpool matching simulation study based on 2008 travel demand data. Both matching strategies are designed to optimize the total system wide vehicle miles travelled and the carpool matching rate, rather than individual preferences. Therefore, the interests of a single carpool user may not be guaranteed from a personal perspective. Carpooling with less number of passengers have a result of low income but have high level of privacy while in case of more number of passengers, income increases but privacy is preserved but not guaranteed. So as first challenge, provide privacy and security to vehicle users among a sufficient number of passengers during sharing of vehicles. Further taking the success of the above carpool studies, a number of mobile-based carpool applications have been developed, such as Lyft, Flinc, Carma, and Tripda etc. These mobile applications provide basic real-time and network-based matching services, and improve the user experience by introducing driver-passenger communication tools, payment modules (for example, fee calculator and payroll system), and security background check functions [5]. These carpooling

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

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Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

services use network-based algorithms to suggest a carpool team and commute route. The matching algorithms are mainly designed for a single driver and a single passenger or systemwide carpool matching optimization. In other words, the algorithms and carpool matching service may not provide an optimized solution when a commuter wants to carpool with more than one person i.e. not provide a certain level of trust and privacy to customers. [10, 11] have argued that two-person carpooling is so inefficient that it may even be considered a burden to the transportation system. Three-person carpooling and vanpooling are recommended. Models, algorithms and services for 3+ person carpooling are needed, but these are still missing in the current carpooling research and literature which is biggest challenges for carpooling schemes. 2.2 Parking Schemes Due to the industrialization of the world, increase in population, slow paced city development and mismanagement of the available parking space has resulted in parking related problems i.e. when there are several vehicle drivers/public transport are on road, then after completing their journey, everyone need safe and secure space to park their vehicle. Every day a significant percentage of drivers/people in single occupancy vehicles are suffering about searching for a parking space for their vehicle. For example, if some people are going for shopping then they needs to park their vehicle near the shopping area which should be safe and secure. Many vehicle users are not interested to park their vehicle far away from their final destination, even if it is secured or privacy preserved. Today’s every drivers need a secure, intelligent, efficient and reliable system which can be used for searching the unoccupied parking facility, guidance towards the parking facility, negotiation of the parking fee, along with the proper management of the parking facility. (Note- here two types of drivers searching parking space, first one who is providing riding to other users and other one is individual drivers.) 2.2.1 History and the Current State In 19’s century, there was less number of vehicles on road but vehicle increases day by day, year by year. Now condition is that each person has at least one vehicle. But we have limited resources/space to park our vehicles. It creates situation of accidents on road or evolution of greenhouses gases (like CO2) due to not non-availability of spaces to park vehicles. This problem is due to increment in population’s ratio, their salary and reduction in fuel prices. To overcome parking problem (during the last four decades), numerous parking search models have been developed (Van der Goot, 1982; Axhausen and Polak, 1991; Polak and Axhausen, 1990; Young et al., 1991a,b; Saltzman, 1997; Shoup, 1997; Steiner, 1998; Thompson and Richardson, 1998; Arnott and Rowse, 1999; Tam and Lam, 2000; Wong et al., 2000; Waterson et al., 2001). A driver can a park his vehicle in passenger or friend home etc. But again here, we need to maintain trust. On the other hand, the drivers usually discover different parking alternatives one by one in a temporal sequence. Clearly, this temporal sequence has a very strong influence on the driver’s final decision about the parking place. Recently in [14], Gongjun et al. proposed a wireless-based intelligent parking system especially applied to large parking lots. This parking system uses infrared sensors to sense vehicles and parking belts to control vehicle entry and exit. Verroios et al. [16] proposed a parking reservation system by deploying sensors on vehicles. Using VANET, the computational infrastructure informs the

vehicle about available parking, but vehicle has to determine the time needed to reach the parking space because that parking space may become occupied by another vehicle in the meantime and no provision exists to inform the first vehicle about the situation. Panyappan [17] proposed a parking system that reduces the extensive and costly deployment infrastructure, and the available parking lots can still be located effectively. But this proposal was restricted to parking lots. Kenny [18] emphasised various applications that widely use DSRC (Dedicated shortrange communications). Further includes the On Board Unit (OBU) and IS (infostation) to extend the concept to ISC (InfoStation Centre) and utilise the high capacity DSRC channel for the comfort application of an on-street parking reservation. Further, during the past two decades, traffic authorities in many cities (Helsinki, Cologne, Mainz, Stuttgart, Wiesbaden, Aalborg, Hague) have started to inform and guide drivers to parking facilities with real-time variable message signs directional arrows [19], names of the parking facilities, status (full, not full, number of available parking spaces, etc.). Current practice shows that parking guidance systems usually do not change the occupancy rate or average parking duration. Drivers easily become familiar with the parking guidance systems [20], and majority of them use, thrust and appreciate the help of the systems. Guidance systems significantly increase the probability of finding vacant parking space, mitigate frustration of the drivers–visitors unfamiliar with the city center, decrease the queues in front of parking garages, decrease the total amount of vehicle-miles traveled (particularly in the city centers), decrease the average trip time, energy consumption, and air pollution [19, 20]. Moreover this, Parking Guidance Systems (PGS) help drivers to find vacant parking spaces when they are already on the network, and approaching their final destination. Parking guidance system is a part of comprehensive parking policy and traffic management system, whose other elements are street parking control (including sanctions for the illegally parked vehicles), parking fare structure, and parking revenue management system. Such systems would help drivers to find a vacant parking space even before beginning their trip. 2.2.2 Influencing Factors Location and schedule requirements seriously limit the convenience and flexibility for parking scheme. Although a large proportion of commuters likely share a similar commute parking space and schedule (thus leading to rush hour traffic), it is difficult to find parking place for each other and coordinate their travel. Therefore, an efficient parking management service which enables commuters to develop a potential parking scheme which can be a critical element in encouraging uses of parking space during rush hours. Some of following factors are to make parking scheme famous include: • Driver compensation (with participant cost minimization): premium fares/charges or a ticketing system, which gives a reason to the user for parking his vehicle. Organization of parked vehicle should be near the user’s offices such that travel time is minimized to reach his office. • Safety controller: verify personal information of users, include a safety device (such as a technology that allows for real time 911 communication) in the vehicle, it have an eye on parked vehicle to provide maximum safety to user’s vehicle. • Publicity: through advertisements, credit mechanisms, governmental assistance or shared screens to increase

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

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maximum use of parking spaces. Information about the number of available parking spaces could be displayed on the major roads, streets and intersections, or it could be distributed through the Internet. It is logical to ask the question about the benefits of the parking guidance systems. • Market segmentation: Market segmentation entails a situation in which different drivers agree to pay different prices for the same asset. For example, it is an example of two types of drivers, a businessman and a pensioner. While a businessman who wants to park a car near a meeting place 15 minutes prior to the meeting is prepared to pay a much higher parking fee, a pensioner planning to walk with his wife through the downtown would make parking reservation a day in advance in order to pay a lower parking fee. However, even the wealthiest businessman would not agree to pay a million dollars for short-term parking. • Privacy Aware Parking System • Local Governments: in many countries, local governments and individual businesses are responsible for forming the parking price for a secure parking. Every day a significant percentage of drivers/people in single occupancy vehicles are suffering about searching for a parking space for their vehicle. On the contrary, when traffic concentrates in a smaller section of the area (for example, a road), the user is faced with a harder trade-off: either he goes for shorter parking search times and routes and parks his vehicle further away from his travel destination (centralized system); or he prefers to spend much more time and fuel in favour of a parking spot closer to his travel destination (opportunistic scheme). Notably, what he gets in the second case is marginally better than he would achieve by randomly wandering around the area of interest since the information circulated by the opportunistic scheme has highly local scope and ends up leveraging the competition amongst the vehicles. With suitable matching algorithms and a culturally receptive owner of parking space and driver base, these three factors should create a viable parking system. 2.2.3 Problems in Parking Models Numerous Parking schemes have been developed with a wide range of approaches and functions. As discussed, provide reliable parking (during rush hours) to vehicles save greenhouses gases, fuel, time and cost of vehicle user (owner). Some models are presented to provide intelligent parking over road network, discussed in section 2.2.1. Now table 1 (refer appendix A) explains different scenarios/problems in parking schemes. Table 3 (refer appendix A) gives a summary of the various existed approaches that have been discussed and highlights their key features to provide a reliable parking space to vehicle drivers. We want to provide services to a target user like i.e. he can inquiry matched vehicles/parking space according to his wishes (nearby by his destination or far away from this destination, according to price or types of services). A negotiation options should be there to make successful business of carpooling/parking. We need such system which can help the economic, social, and safety based aspects to the security i.e. it preserve the environment i.e. greenhouses, fuel, time and number of vehicles on road. However in parking, we can provide some discount/credit facilities to attract more users’ i.e. to reduce traffic on road or make this business successful. Hence this section discusses about the literature review with respect of parking and carpooling in detail. It covers required

successful parameters and arise problems in parking and carpooling schemes. Now further section will deal with motivation related to this work. III. MOTIVATION 3.1 Why Carpooling Services? The paper extends the reasons for carpooling: these include savings in travelling expenses, limited parking space, and poor transit service for individual who cannot drive. The costs and travel times are always increasing and that the mortality rate due to accidents in urban areas is very high. From a macro-economic point of view, the society "pays" a very high cost for urban mobility and each user adapts its travel patterns [21] in order to reduce the structural deficit of the transport network. For example, several authors have approached the matter with the goal of mitigating the consequences of individual mobility trying to model the feature of traffic that occur during peak hours. As discussed, main problem is that maximum vehicle come out with their vehicles during peak hours to create congestion/jamming problem. So first, we have to rectify this problem using carpooling schemes among various people. Further we can provide to them privacy preserved and secure parking (at reaching their destination). But someone can carpool with other user only if he got guaranteed privacy preserved and trusted journey. An attention has been paid on potentials of increasing carpooling for reducing fuel consumption and traffic congestion and improving air quality. The authors threw up some interesting fact about why carpooling. Without carpooling, the amount required for 968,316 kiloliter petrol for 1.3 mn cars is Rupees 4,213 crore per annum. By carpooling, this amount reduces to Rupees 2902 crore. Thus, revenue of Rupees 1310.98 crore can be saved by saving 301,307 kiloliter petrol by carpooling. Similar cases have been studied on traffic of Delhi, Shanghai in twenty century. Further in even–odd formula (applied on vehicle numbers), which was applied in Delhi (in January and April 2016), people need to share carpool or public transportation to reach their destination (to do their job). Todays the condition of various cities like Delhi, Shanghai, New York etc. are becoming worst due to high pollution. As a perfect solution for this problem (reducing the total number of vehicles over road), use public transport or make a carpool with other vehicle to do your daily work. Using carpooling saves fuel, time, parking spaces, and decreases the number of accidents around the world. As discussed in section 2, decreasing of fuel prices or highly income of people raised pollution in the environment by using separate vehicle on road. Several reasons to use carpooling are: • To decreasing the number of cars travelling to/from a destination; • To broadening the possibilities to intervene in travelling for longer distances (more than 3 kilometers); • To contribute to environmental quality. • To decrease of travel costs • To provide benefit from other company organized measures such as special parking spaces. Moreover this, carpooling systems present two main characteristics with two types of variants (refer table 2, in Appendix A): • Static nature: in this, trips are scheduled several days in advance, but no interaction between users (such as picking up a new passenger on the fly) is possible once the trip has started.

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•

Centralised infrastructure: in this, applications rely on fixed pre-established Trusted Third Parties (TTP) in charge of collecting and storing sensitive information from carpooling users (such as their identity, location, and usual trips) [22]. • Others: Some typical features of ridesharing system are: i.e. arrangement of one-time trips instead of recurrent appointments for commuters; the usage of mobile phones for placing ridesharing requests and offers; and automatic and instant matching of rides through a network service. 3.1.1 Different Situations for Carpooling Users As discussed, users want a private ride from one location to other location. For that, users should travelled some distance by walk (i.e. covering some distance to reduce jamming problem) and collect on particular stops defined by carpooler/driver side. Various scenarios are invented for carpooling which are defined as: • Vehicles pick up customer from different origins and drop also to different destinations. • Vehicles pick up customer from same origins and drop also to same destinations • Vehicles pick up customer from same locations and drop also to different destinations. • Vehicles pick up customer from different origins and drop to same locations. Functional carpooling entities: Regardless of the final implementation of the system, the infrastructure less nature of dynamic carpooling requires the consideration of three main entities [22]. • Driver: The driver corresponds to an active user of the carpooling application that possesses a vehicle and is willing to carpool with another user on some part of her itinerary. A user declares himself as a driver by activating the corresponding mode on the carpooling application. • Passenger: The passenger is an active user of the carpooling application that does not use his own vehicle and would like to be match with a driver whose itinerary matches her own mobility desiderata. A user declares himself as a passenger by activating the associated mode on the carpooling application. • Intermediary: The intermediary is a passive user in charge of forwarding messages from one geo-region to another, consequently enabling distant users to communicate. The key role of intermediary nodes is to improve the connectivity of the carpooling network. Non-functional entities for carpooling • Prover: A user who wants to convince others of her current location while preserving her anonymity behind a unique pseudonym. • Witness: It referred also as watchdog node is a user in the local broadcast vicinity of a prover who acknowledges her legitimate location. • Verifier: A user who checks the location announced by a prover in a distant geo-region and acknowledged by their witnesses. • Certification Authority (CA): A trusted third party to assign credentials to new users of the carpooling application. The role of this entity is limited to the registration time, but ensures the property of authenticated identity during the execution of carpooling activities.

•

Anonymity Lifter (AL): This entity is a trusted third party (TTP) used to lift the anonymity of users when legally required (for example, to sue a given user). This is a passive entity in the sense that it does not participate in carpooling activities. Nevertheless, the AL is essential to ensure non-repudiation. 3.1.2 Information and Tips for Successful Carpooling Once you have matched and met your new car pool partner(s), these simple questions will help avoid problems over road and will make carpooling successful: • Where will the designated pick-up point be? Some car pools have door to door service, while others prefer to meet at a convenient church or grocery store parking lot. • Establish smoking/non-smoking policies. • Is eating/drinking allowed? • Which station will you listen to on the radio? News channels are usually great ideas for updates on traffic, sports, stocks and weather. • Will there be any unscheduled stops? • Who will be your alternate drivers in cases of illness or emergency? • How long will you wait for someone if they are late? 3-5 minutes is usually recommended. • How many days a week will you share the ride to work? There are car pools operating every weekday, and some that run just once or twice a week. (Note-To make successful carpooling scheme, we should provide privacy preserved and trusted carpooling services to every users whoever using this service). All above discussed factors have a major role to make ridesharing scheme successful. Knowing about weather (or temperature)/road conditions also have a major factor to make carpooling successful. 3.2 Why Parking Schemes? The concern of mobility in urban areas in recent decades has become an increasingly serious problem and difficult to manage: the quality of life, not only of drivers also other people is strongly influenced due to inefficiencies and diseconomies of urban congestion [21]. This raises also serious problems of pollution and noise and wide spaces of the city are occupied by parked cars. Generally, Parking areas are places of change of transport modality where user switch from vehicle to pedestrian mode or from car to public transport mode, where the destination of the vehicle is the parking lot but it is not univocally determined. In fact, the motorist in making their choices of path takes account of a subset of parking spot and where possible the choice of parking that will access depends on the availability of parking stalls when user enters [21]. Two type of parking predictive models exited for vehicle users to park their vehicles: • In the static predictive models, the model simulates the assignment of drivers over road network where each parking lot is assigned to the respective capacities and for different time periods is possible to determine the filling level of the parking lots at various times of the day, providing for the parking of a newly designed prediction on usage. • In dynamic predictive models, the model wants to predict the saturation of the parking lot. Usually, it is available the historical data on the number of stalls occupied (for example, every 10 minutes) in the working day average winter and sensors for the control of vehicles on road

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network and / or at access points to the entry and exit, in the case of private parking lots, to update continuously the potential demand for parking [21]. The model applies the temporal evolution of historical data to traffic measured in real time at a given time of the day, and determine a hypothesis of parking lot usage, usually with an advance of 20-30 minutes. This hypothesis is iteratively corrected every 10 minutes. The prediction provided by the models is used as input for addressing the parking of a dynamic type that "guide" motorists to parking spaces available via variable message panels creating a dynamic navigation system search for parking [21]. Thus, the number of parking demands, which depict different groups of drivers over time, depend on the manner in which market segmentation has previously been carried out (such as defining two, three, or ten different types of drivers and parking), as well as parking fees that apply for certain types of parking. Various models used to address parking lot management which provides decision support to each driver who being in the area of origin wants to reach a place of destination in the shortest possible time, having the need to park his vehicle [5]. This work needs a parking space that should be optimal with respect to place of destination and also in the area of origin (for example, in peak hours). 3.2.1 Different Situations for Parking Therefore, the parking management involves supply and organization of the private parking in designated areas or along the public roads. It represents not only an important strategic measure, but also specific action plans for control and management of urban traffic. In general, the parking supply management must find the tools to control the number and nature of requests for parking and actions that affect the spatio-temporal allocation of stalls [21]. Such activity is evident that influence traffic conditions on roads, determining an appreciable quality of flow and congestion. Different situations for parked vehicles can be used for carpooling as: • Vehicles with different origins (i.e. parking) and destinations. • Vehicles with same origins and destinations • Vehicles with same origins but with different destinations. • Vehicles with different origins but with same destinations Functional parking entities • Driver: The driver corresponds to an active user of the parking application that possesses a vehicle and is willing to park with another user on some part of her itinerary. Vehicle user declares himself as a driver (parking user) and declaring herself as an interested parking provider. • Intermediary: The intermediary is a passive user in charge of forwarding messages from one geo-region to another, consequently enabling distant users to communicate. The key role of intermediary nodes is to improve the connectivity, provide reliable and guaranteed parking. • Attacker: In the context of vehicle parking, the attacker is one (or more) compromise entity that wants to violate successfully the security of honest vehicles by using several techniques to achieve his goal (for financial gain via leaking of user’s personal information, sending false messages to users etc.). Non-functional entities for parking • Certification Authority (CA): A trusted third party to assign credentials to new users for parking. The role of

this entity is limited to the registration time, but ensures the property of authenticated identity during the execution of parking schemes. • Anonymity Lifter (AL): This entity is a trusted third party used to lift the anonymity of users when legally required (for example, to sue a given user). • Guidance System (GS): It used to put an eye on vehicle for proving security. It known also as watchdog node/witness, i.e. a user in the local broadcast vicinity of a prover who acknowledges her legitimate location. Now days, searching for secure parking space is a time consuming process which not only affects the economic activities efficiency, but social interactions and cost also. Road pricing/parking place is in many countries indeed an important political issue. The right to move a car is superior to the right to store cars on the public way. In many countries, local governments and/individual businesses are responsible for forming the parking price. While off-street parking pricing is often controlled by individual companies. A successful combination of road and street (public and private) parking pricing will immensely enable to mitigate congestion, distribute traffic flows more evenly through time and space and decrease average travel time and cost. This section discusses about carpooling and parking problems; various situations arise in respective schemes; functional and non-functional entities related to carpooling and parking schemes. Now next section discusses about various challenges counted in carpooling and parking schemes. IV.

CHALLENGES IN DYNAMIC CARPOOLING AND VEHICLE PARKING SCHEMES 4.1 Challenges in Dynamic Carpooling Services Carpooling was early introduced in 1970s, but it is in the last 5 years that it has gained much momentum in our society [22], especially in countries with high population density. For example, recent studies carried out in China [23] state the increasing interest of society in carpooling solutions, highlighting the cost saving and congestion reduction as the most important benefits. However, to address its dynamic decentralised deployment [22] it is necessary to face some issues. Therefore, preserving location privacy of passengers; trust between customer and drivers (in a ridesharing scheme) is a major challenge limiting the possibilities offered by the mobiquitous setting to provide efficient and trusted geo-services. Location Privacy and Trust has been a serious concern for mobile users who used location-based services [26] during carpooling. Location-Based Services (LBSs) provide mobile users with valuable (can be confidential) information about their surroundings such as traffic status (for example, Beat the Traffic, or INRIX Traffic Maps, Routes & Alerts), nearby points of interest (for example, Google Maps), or friends' activities (for example, Foursquare or Google Latitude) etc. In simple form, LBSs deliver information to a vehicle user based on his/her current physical location [27]. Location privacy is an important issue in vehicular networks since knowledge of a vehicle’s location can result in leakage of sensitive information [27]. The AMORES project 2 [24] is an initiative that addresses the challenges of dynamic carpooling from a cooperative and distributed way. This work addresses the carpooling problem as a major problem for VANET users. In overall, they involve improving the performance and the comfort of the carpooling

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experience with protecting the user (driver and passenger) privacy. 4.1.1 Scheduling the Meeting Point One of the main bottlenecks of carpooling is scheduling the meeting point. In traditional carpooling this decision is taken jointly between the driver and the passenger days before the trip [22]. However, in practice, the system does not provide effective mechanisms to warn users about delays. Carpooling problem identified as complex for example, Amit is aware of arriving 30 min late, but just notifies Anil a delay of 5 min to force him to wait. To avoid these annoying situations, it would be necessary that Anil could validate Amit’s real position to really trust her. Furthermore, this validation would limit the delays caused by the selection of confusing meeting points. One of the challenges for dynamic carpooling consists in providing mechanisms so that scheduling the meeting point becomes a more reliable task. 4.1.2 Privacy aspects The work done in [8] revealed privacy risks as an important drawback of current carpooling systems. The fact that the collection and transmission of personal data can also be used against the privacy of users, either at the transmission time (for example, to send unwanted advertisement), or later in the future, is seen by users as an important threat. This problem becomes especially important in dynamic carpooling as messages use the wireless medium and attackers may be equipped with eavesdropping capacities. Currently, there is no universal location privacy mechanism that has reached a consensus in the privacy community. Recently, some cooperative schemes for neighbour position verification were proposed. From the point of view of privacy, authors of [12] ensure certain degree of communication anonymity by relying on random MAC (Medium Access Control) address generators during the discovery phase in order to obfuscate the identity of the users. Yet, this protocol assumes that the verifier is trusted (i.e., honest). In [13, 29] authors propose a privacy-preserving location verification mechanism called APPLAUS (A Privacy-Preserving LocAtion proof Updating System). Yet, the solution relies on a centralised Trusted Third Party (TTP). The challenge for dynamic carpooling consists in developing distributed TTPs that can replace centralised ones to protect user’s privacy. 4.1.3 Trust among users The potential dispute between users is a significant problem identified in [23]. This problem has been addressed from a prevention view point in traditional carpooling i.e., by proposing matching mechanisms to enhance the compatibility among the driver and the passengers. Nevertheless, to date, there is a lack of trust mechanisms that may protect users in case of needing to reclaim legal responsibilities to other party involved in the carpooling activity. For example, if during a police investigation Anil denies having carpooled with him, Amit should be able to prove that he was with Anil during a certain period. Dynamic carpooling has the potential to collect location proofs that could be used as evidences by offended users [22]. We need a trust model to provide a certain level of trust to carpool user, also to vehicle driver. However, “how to do it while respecting the principle of privacy is a challenge that has not been addressed before from a distributed and collaborative way in the domain of carpooling”? Some problems to be address related to trust in carpooling mechanism which is notified as biggest challenges: • What are the methods used in the proposed trust models? • What are the trust metrics used to measure trust in the existing trust model?

• What are the properties of the trust model? 4.1.4 Choosing Passengers During pool ‘up, if the set of drivers or passengers is known in advance, then for any car capacity, the problem is equivalent to the assignment problem in bipartite graphs. Otherwise, when we do not know in advance who will drive their vehicle and who will be a passenger, the problem is NP-hard. Some questions arise here regarding carpooling with respect to choices of passengers, summarized as: • Would you consider sharing a taxi with any institute affiliate (faculty, staff, or student)? • Would you consider sharing a taxi with any smokerperson/ or with a faculty, staff, or student? • Would you consider sharing a taxi with any person (not necessarily from any institute/university affiliated)? • Would you consider sharing a taxi with an institute affiliate of your same type (for example, if you are a student then you will prefer to share a cab only with students, not with staff or faculty members)? • If you were to share a taxi with others, with “how many people would you be comfortable sharing a taxi”? • Would you feel comfortable sharing a taxi with one of the following: persons of your same gender, persons of the opposite gender, or both? • Would you feel comfortable sharing a taxi to and from your residence? • Would you feel sharing a taxi with any person with minimum cost? As conclusion, in ridesharing mechanism, security and privacy of drivers and passengers, safety of passenger, trust among passengers and drivers, publicity for carpooling services, negotiable price, gender equivalency and gender diversity are major effective factors to make carpooling schemes successful. 4.2 Challenges in Parking Scheme Every day a significant percentage of drivers in single occupancy vehicles search for a parking space. Various challenges are countermeasure in parking problem for vehicle users. Some are described as: • Finding parking on genuine/ negotiable price • Availability of privacy preserved secured parking place • Parking fine (collection of toll charges, or fine etc.) • Confidentiality of user’s personal/route/location information • Traffic management/management of parking facility Here Security means parking control systems to keep an eye/check on any violations on parking lot. A Parking schemes over road/street/particular place can be one or sided/double sided. But parking with double sided (over a road network) create several problems, which can be summarized as: • increased traffic on residential streets when motorists ratrun to avoid congestion on major corridors; • degradation in the quality of bus services in the locality; • increase accident risk for pedestrians when double-parked cars block sight lines; • safety concerns for motorists and cyclists manoeuvring around double-parked cars; • elevated pollution levels; • unnecessary use of fuel and elevated carbon emissions.

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4.2.1 Actions to provide reliable parking in an urban area Actions that can be taken to provide secure, trusted parking in an urban area (during rush hours) define as: • Eliminate the parking stalls along some roads and regulation of parking only a few hours (for example, peak hours); • Adjust the number of stalls in public areas already equipped or be devoted to parking; • Determine and differentiation parking rates depending on the length of time for parking, • Define the location of areas suitable for intermodal transfer. 4.2.2 Factors to evaluate the effects of the parking management strategies In order to evaluate the effects of the main parking management strategies, it is necessary to estimate: • Difficulty in reaching a parking lot; • Level of use of a particular parking lot (occupancy rate); • Impact of changes in demand for parking (number of arrivals and duration); • Changes in the provision of parking areas (locations and number). 4.2.3 Effects of providing parking on different spatial charge The choice in the parking management about different spatial charge, particularly in central urban areas can be: • A decrease in the number of parking spaces occupied in an area; • An increase of parking spaces occupied in the surrounding areas; • Changes to the areas of congestion, • A change in the average walking time, • A change of modal split, Hence this section discusses various challenges arises in ridesharing and parking schemes. As conclusion of this section, trust and security are two major factors to make parking scheme successful. Now next section will deal with future works with respect of carpooling and parking schemes. V. FUTURE WORKS The future of dynamic carpooling requires to paying attention to more practical aspects combining privacy, resilience and trust issues with performance aspects. As discussed above, various different vehicles (car, truck, buses etc.) are running over road and creating several problems like accidents, jamming, congestion, pollution etc. So we proposed a special scheme to overcome above problems i.e. carpooling i.e. sharing of vehicles to reduce the large number of vehicles on road. Similarly when there are a lot of vehicles over road then we proposed another scheme i.e. providing of secure and trust enabled parking space to vehicle users over road/street network. (Note- This work discusses and provides efficient, reliable parking services to both users i.e. individual and carpoolers). In consequence, it is necessary to find a feasible trade-off between the different dimensions of the problem taking into account the resourcelimited nature of small mobile devices [22]. This point is essential so that the application becomes useable and thus attractive for people. The idea of carpooling would be an excellent gateway to cover the gap between private cars and public transportation. We should not think about dynamic carpooling as a competitor for public transports or B2C (business to customer) transportation systems but rather as the perfect

complement. We are ambitious to explore the privacy-by-design concerns from social cars to the future of social travelling. Several future works in carpooling area can be summarized as: • Real time traffic information during pool’ up; • Best route search based on traffic information, based on dynamic route matching algorithms; • Integration with public transportation information and parking facilities; • Pre-booked parking place; • Ad hoc trip arrangements; • Use of past-experience data to estimate time-to-pickup; • User Profiles and credit mechanisms. Further, we have three major questions related to provide a safe and privacy preserved carpooling: (a) How you will find a genuine or trusted passenger/driver during carpooling? (b) How you will authenticate to a passenger just before a moment of carpooling/ridesharing i.e. is it authentic/real user or not? (c) How you will provide the interfaces between the different transports in such a way the privacy-by-design concept is preserved? Moreover this, todays Cell phones, Personal Computers, and Internet have made a revolution in numerous daily activities. People can communicate to other route using cell phone/internet network. Further we can use these smart technologies to find a secure and privacy preserved parking area for our vehicle. In parking schemes, possible objective function would be to maximize the number of accepted requests from one or more request categories. An intelligent parking system should be proposed which could represent further improvement in modern parking technologies [19] including parking garages in cities, at big international airports or near popular places. An approach, which follows the privacy by design principle, integrates the privacy aspect in the design of the system should be proposed as future work. For future work, [17] can be extended by a further reduction in infrastructure and apply the architecture to on-street parking zones that demand a slightly more complex management structure compared with parking lots. Additionally, the concepts of the parking reservation system and parking revenue management system can also be proposed as future work. This could be also of great help in our attempts to solve complex parking problems. Besides that, there are a lot of questions that need to be answered in future work. Several future work related to Parking can be summarized as: • The parking availability information system and parking reservation system should provide advanced navigation services. • The mobile electric commerce system and a continuously working gate system should collect the toll charges electrically [25]. • An automated navigation system should assist in safe driving. • An in-facility navigation system should provide the best possible traffic management. • Provision of effective security for the safety of cars. • Provision of strong functions for facilitating administrators and managers in management of the parking facility. • The proposed ‘‘intelligent’’ parking space inventory control system should be based on the combination of simulation, optimization techniques [19], and fuzzy logic

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makes “real-time” decisions as to whether to reject or accept a new request for parking. Many drivers consider market segmentation as biggest factor for parking i.e., parking fees at particular locations at particular time periods, as additional taxes. As concluding of this section, further research would be done to check if intelligent parking systems will be able to further decrease the queues in front of parking garages, the total amount of vehicle-miles traveled, the average trip time, energy consumption, and air pollution. This section mentions various future works related to carpooling and parking problems. Now next section concludes this work in brief. VI. CONCLUSION The concern of mobility on road in recent decades has become an increasingly serious problem and difficult to manage. The biggest reason for that is decreasing in fuel prices and high income of people. This makes an effect on society directly i.e. on their living standards. This work has explored the potential of cars to inspire new types of social interactions to join ridesharing and parking on road. Carpooling (dynamic) is a novel social-inspired service offering drivers and passengers the possibility to easily share a car. In result, this scheme reduces cost, fuel consumption, pollution etc. With increasing population, the emphasis to research new alternatives to efficiently reduce traffic jams and carbon emissions in cities is only going to grow, also our planet becoming more populated. We need our cities need to evolve smartly and should be pollution free. Vehicles sharing are not only going to be a convenient way of travelling in the future, it is going to become a necessity. Further, a main role of any parking strategy/service should be to reduce the total number of vehicle trips during certain time periods or total number of vehicles on road, especially in peak hours. This is indeed one of the essential public sector objectives. This work discusses that revenue can be maximized with constraints associated with protected number of parking spaces which is reserved for particular drivers, as well as parking types. By simple introduction of specified restrictions, it is feasible to protect a reserved parking spaces for elderly; handicapped person and pregnant woman etc. Maximization of revenue for the parking management represents an attempt to employ basic market principles and to achieve and maintain equilibrium between transportation supply and demand. Last but not least in carpooling and parking schemes, some restrictions should be maintained for special category people like senior citizen, disabled people and pregnant ladies etc. should give some priority to make their day happier. As future work, our biggest challenge is to protect our planet form greenhouses gases by reducing the traffic (i.e. reducing total number of vehicles over road network using carpooling and parking schemes) and need to provide convenient services to public with efficient and reliable results. ACKNOWLEDGEMENT

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The authors have declared that they have no acknowledgement. CONFLICT OF INTERESTS The authors declare that there is no conflict of interests regarding the publication of this paper.

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A. Boukerche et al., Vehicular Ad Hoc Networks: A New Challenge for ..., Comput. Commun,2008. DOI:10.1016/j.comcom.2007.12.004. Irith Ben-Arroyo Hartman, Daniel Keren et al., Theory and Practice in Large Carpooling Problems, 5th International Conference on Ambient Systems, Networks and Technologies, Procedia Computer Science 32, pp. 339 – 347, 2014. http://arxiv.org/pdf/1306.0361.pdf http://pro.unibz.it/library/thesis/00004801_11379.pdf Jizhe Xia, Kevin M. Curtin, et al., A New Model for a Carpool Matching Service, PLOS ONE, DOI:10.1371/journal.pone.0129257 June 30, 2015. Chan ND, Shaheen SA, Ridesharing in North America: Past, Present, and Future. Transport Reviews, 32(1), pp.93–112, 2012. DOI: 10.1080/01441647.2011.621557 Ferguson E, The rise and fall of the American carpool: 1970– 1990. Transportation, 24(4), pp.349–376, (1997a).. DOI: 10.1023/A:1004928012320 Shewmake S, Can Carpooling Clear the Road and Clean the Air? Evidence from the Literature on the Impact of HOV Lanes on VMT and Air Pollution. Journal of Planning Literature, 27(4), pp.363–374, 2012. Cassidy MJ, Jang K, Daganzo CF, The smoothing effect of carpool lanes on freeway bottlenecks. Transportation Research Part A: Policy and Practice, 44(2), pp.65–75, 2010. DOI: 10.1016/j.tra.2009.11. 002 Small KA, Winston C, Yan J, Baum-Snow N, Gómez-Ibáñez JA, Differentiated Road Pricing, Express Lanes, and Carpools: Exploiting Heterogeneous Preferences in Policy Design [with Comments]. Brookings-Wharton Papers on Urban Affairs, pp.53–96, 2006. DOI: 10.2307/25067428 Poole RW, Balaker T, Virtual exclusive busways: improving urban transit while relieving congestion. Los Angeles, CA: Reason Foundation, 2005. Rasheed Hussain and Heekuck Oh, Cooperation-Aware VANET Clouds: Providing Secure Cloud Services to Vehicular Ad Hoc Networks, J Inf Process Syst, Vol.10, No.1, pp.103~118, March 2014. Agatz NA, Erera AL, Savelsbergh MW, Wang X, Dynamic ride-sharing: A simulation study in metro Atlanta. Transportation Research Part B: Methodological, 45(9), pp.1450–1464, 2011. G. Yan, W. Yang, D. B. Rawat and S. Olariu, "Smart Parking: A Secure and Intelligent Parking System," in Intelligent Transportation Systems Magazine, IEEE, vol. 3, pp.18-30, 2011. Alhammad, Abdulmalik, Siewe, Francois, An InfoStationBased Context-Aware On-Street Parking System, IEEE, 2013. H. Moustafa and Y. Zhang, Vehicular Networks: Techniques, Standards, and Applications. Boston, MA, USA: Auerbach Publications, 2009. R. Panayappan, J. M. Trivedi, A. Studer and A. Perrig, "VANET-based approach for parking space availability," in Proceedings of the Fourth ACM International Workshop on Vehicular Ad Hoc Networks, Montreal, Quebec, Canada, pp. 75-76, 2007. J. B. Kenney, "Dedicated Short-Range Communications (DSRC) Standards in the United States," in Proceedings of the IEEE, vol. 99, pp.1162-1182, 2011. http://www.iasi.cnr.it/ewgt/13conference/5_teodorovic.pdf http://wenku.baidu.com/view/334d23bdc77da26925c5b088.ht ml Tullio Giuffrè, Sabato Marco Siniscalch, Giovanni Tesoriere, A novel architecture of Parking management for Smart Cities, SIIV - 5th International Congress - Sustainability of Road Infrastructures, Procedia - Social and Behavioral Sciences 53, pp.16 – 28, 2012.

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[22] Jesús Friginal, Sébastien Gambs et al., Towards privacydriven design of a dynamic carpooling system, Pervasive and Mobile Computing 14, pp.71–82, 2014. [23] Wenhui Xin, Shunying Zhu, Hong Wang, Yongfei Yan, Jining Xiong, Analyzing early market potential and strategies for carpooling in China: a case study of wuhan, in: International Conference on Management and Service Science, 2009, MASS ’09, pp.1–4, 2009. [24] Christian Artigues, Yves Deswarte, Jérémie Guiochet,et. al., AMORES: an architecture for mobiquitous resilient systems, in: Proceedings of the 1st European Workshop on AppRoaches to MObiquiTous Resilience, ARMOR’12, ACM, pp. 7:1–7:6, 2012. [25] Mahmud, S A, Khan, G M, Rahman, M, Zafar, H, A Survey of Intelligent Car Parking System, Journal of Applied Research and Technology, 2013. [26] Amit Kumar Tyagi, N. Sreenath, Preserving Location Privacy in Location Based Services against Sybil Attacks, IJSIA Vol.9, No.12 (2015), pp.189-210, December, 2015. [27] Amit Kumar Tyagi, N. Sreenath, A Comparative Study on Privacy Preserving Techniques for Location Based Services, BJMCS, July, 2015 10(4): pp.1-25, 2015. [28] Amit Kumar Tyagi, N.Sreenath, Never Trust Anyone: TrustPrivacy Trade-Offs in Vehicular Ad hoc Network, BJMCS 2016. [29] Zhichao Zhu et.al, APPLAUS: A Privacy-Preserving Location Proof Updating System for location-based services, INFOCOM, Proceedings IEEE, 2011. DOI:10.1109/INFCOM.2011.5934991 [30] Amit Kumar Tyagi, N.Sreenath “Providing Trust Enabled Services in Vehicular Cloud Computing (extended version)”, ICIA, ACM, 25-26 August, Pondicherry, India, 2016. DOI: http://dx.doi.org/10.1145/2980258.2980263.

Table 2 Variants of Ridesharing System Driver Type Single driver

Message Type Availability of parking space Communication o interest

Parking assignment Confirmation of the parking assignment Request for new coordinator Answer to the request for new coordinator

Problem (If Lost) -

Impact -

Very low Problem 1: Sub-optimal allocation Problem 2: Unnecessary election of a new coordinato Low (if all the communications o interest get lost) → increase of delay Problem 3: sub-optimal Very low problem Problem 4: multiple assignment of the parking space -

Process of allocation of parking space

Problem 5: Sub-optimal Very low allocation Problem 6: Restart of the process to elect a new Low coordinator (if all the communications o interest get lost)→ increase of delay Medium Acceptance of the Problem 7: Multiple coordinator’s role coordinators → Multiple assignment of the parking space

Simple matching between driver/rider

Table 3 Summary of relevant techniques for parking systems in an intelligent way S. Different No. Technologies 1 Agent Based

2

Fuzzy Based

3

Wireless Sensor Based

4

GPS Based

5

Vehicular Communication

6

Vision Based

Features

Services Provided

Bargaining, parking guidance and route negotiation etc. Intelligent parking methods, for example, parallel parking and perpendicular parking etc. Low cost implementation Detection and with lower monitoring of the power consumption parking facility etc. Real time location based Provides information information about the locality and guidance towards and availability of destination parking facility Antitheft protection, Provision of parking real information time parking distribution service for navigation service mobile vehicles etc. Good for car searching in Lot occupancy large detection, parking parking lots space recognition, parking charges collection etc.

Dynamic Distribution and Complex Traffic Environments Human-like intelligence and expertise

AUTHOR’S PROFILE

Medium

-

Multiple Rider

One pick up and drop off location, Multiple pick up and drop off locations. Multiple driver Rider is transferred between Routing drivers, drivers routing riders.

APPENDIX-A Table 1 Summary of potential problems due to message losses in parking

Single Rider

Process of selection of a new coordinator

Amit Kumar Tyagi is currently working as Ph.D Research Scholar (Full-Time) in Pondicherry Engineering College, Puducherry. He has completed his M.Tech in Computer Science and Engineering from Pondicherry Central University, Puducherry, in 2012. His research interests include Smart and Secure Computing, Network and Information Security, Theoretical Computer Science, Privacy (including Genomic Privacy), Evolvable Hardware, Parallel Algorithms and Cloud Computing. Amit Kumar Tyagi can be contacted at: [email protected]

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Dr. N. Sreenath is currently working as Dean (A&A) and Professor in Pondicherry Engineering College, Puducherry. He completed his PhD in Computer Science and Engineering from Indian Institute of Technology, Madras, in 2003. His primary research interest lies in WDM Optical Networks and High speed networks. Dr. N.Sreenath can be contacted at: [email protected]

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Efficient Energy Utilisation in Zigbee WDSN using Clustering protocol and RSSI Algorithm. Maria Brigitta.R

Samundiswary.P

Department of Electronics Engineering, School of Engineering and Technology Pondicherry University Puducherry-605 014, India [email protected]

Department of Electronics Engineering, School of Engineering and Technology Pondicherry University Puducherry-605 014, India [email protected]

Abstract — In the era of wireless communication, Zigbee based Wireless Sensor Networks (WSNs) prove to be a promising technology in various fields of applications such as surveillance, health monitoring etc. In most Zigbee based WSNs, energy consumption is of major concern as the nodes have limited battery power. In the proposed work, Zigbee based Wireless Dynamic Sensor Network (WDSN) is considered; where in the nodes are imposed with mobility. This enhances the nodes independency. Thus the proposed work is divided into two phases. In the first phase, the distance between the mobile nodes is determined by means of the Received Signal Strength Indicator (RSSI) algorithm and then, the energy level to the nodes is adapted based on the calculated distance. In the second phase, the residual energy is calculated for each node in a cluster and a clustering protocol is used to identify the cluster head by choosing the node that has the highest residual energy in the cluster. The performance of proposed work is analyzed through evaluation of metrics such as average residual energy and throughput. The simulations are done using ns2 and it is observed that the proposed work not only ensures efficient energy utilization but also enhanced throughput is attained. Keywordsefficiency.

WDSN;

I.

Zigbee;

RSSI;

clustering;

energy

INTRODUCTION

A Wireless Sensor Network deploys numerous tiny, inexpensive and battery powered nodes which have the capability to sense, compute and interact with other nodes in order to gather information about the location and make a decision about its physical environment. Wireless Sensor Networks are deployed for a wide range of applications such as remote location monitoring, security surveillance, military, health monitoring etc [1-6]. The sensor nodes in WSN sense physical parameters such as temperature, pressure, humidity etc. The data sensed by the sensor nodes is passed on to the sink where in the processing and aggregation of sensed data takes place. Furthermore, the sink forwards the processed data to the task manager. The task manager which is also known as base station is a centralized point of control within the network. It extracts information from the network and disseminates information back into the network. WSNs pose several advantages but they do suffer from few limitations [7-13]. Among them, energy-efficiency is one of the major concerns since the nodes are limited in their battery power. Also, in Zigbee based Wireless Dynamic Sensor Network the nodes are mobile; hence the energy consumed by the sensor nodes varies with their

distance. Therefore, the energy to the nodes should be adapted according to their distance. This can be effectively done through the use of Received Signal Strength Indicator (RSSI) algorithm. The RSSI is best suited for wireless communication. In the RSSI algorithm, the received signal strength is inversely proportional to the squared distance between the transmitter and the receiver. Thus it can be used to measure the quality of the wireless link. Based on the RSSI, the packet reception rate of a network can be determined. That is, a threshold is maintained while calculating the RSSI. If the RSSI window overlaps with the threshold region, then the packet error rate is unpredictable and if there is no RSSI window overlaps with the threshold region, it indicates that the packet error rate is stable and low. In order to enhance the energy efficiency, various approaches have been proposed. Of them, clustering the sensor nodes in the sensor networks depicts better performance. Clustering approach proves to be efficient since the cluster head aggregates the data collected from its neighbor nodes, removes the redundant data’s, compresses them and finally forwards them to the centralized access point. These enable the sensor network to achieve better energy utilization. Further, the dynamics in the WDSN pose a serious challenge on the formation of the network, self-organizing capability, discovering the route and managing the communication between the nodes. Thus, WDSN requires efficient protocols which will overcome the above mentioned challenges. A Zigbee based WDSN [14] helps to overcome the challenges posed by WDSN. The IEEE standard of Zigbee network is IEEE 802.15.4 that defines low rate, low power network for wireless application. Zigbee defines the network layer that enables to support multiple hops and routing between the sensor nodes. First, for energy management, the Zigbee protocol defines a hibernation feature to the sensor nodes (i.e.) the nodes enter into sleep state when they are idle. This is an important feature for Zigbee based WDSN. Next, WDSN requires a self-organizing functionality. Zigbee protocol has built-in functions for discovering the network and its formation and also routing the data’s. Thus the Zigbee based WDSN supports the self-organizing capability of the traditional WDSN. In the recent years, few authors have proposed several protocols for existing WDSN networks [15]. These protocols either aim at reducing energy consumption or at

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enhancing the throughput. Hence in this paper, an attempt has been made to develop an efficient protocol for Zigbee based WDSN by reducing the energy consumption as well as enhancing the throughput. This protocol is developed by utilizing an algorithm which elects the cluster head based on residual energy of the sensor nodes which is discussed in this paper. The rest of the paper can be summarized as follows: Section II reviews the concept of existing work. Section III presents the proposed work. Section IV deals with the analysis of the network performance of the proposed work through simulation results. Finally, Section V concludes the paper. II.

III. PROPOSED WORK The proposed work is based on the Zigbee based Wireless Dynamic Sensor Network. It uses an RSSI algorithm and a clustering protocol to ensure energy efficiency and throughput enhancement when the nodes are mobile in the network. The proposed work involves the following mathematical calculations: •

The distance between any two nodes is calculated using ( − ) + ( − ) .

•

Average Energy consumed for transmitting control packets = (Total Energy Consumption /d). Here d denotes the total number of divisions at which the distance among the nodes is divided in order to determine the energy required to transmit the control packets. In the proposed work, the total distance (from 0 to 1000m) is divided in range of 100m. Therefore d takes the value as 10 in the proposed work.

EXISTING WORK

In a Wireless Sensor Network several reliable routing schemes have been proposed [16]. The major categories of such protocols are: Flat protocol, Hierarchical Protocols and Location based Protocols. Among these protocols, the Hierarchical protocols aim at reducing the size of the routing tables [17]. In these types of protocols, the nodes are grouped into clusters and the node which has the maximum energy becomes the cluster head of that particular cluster. The routing protocols based on clusters include two phases, namely the set up phase and the steady state phase. During the set up phase the nodes are grouped into clusters and a cluster head will be elected among them. During the steady state phase, the nodes under a cluster sense the data continuously and send them periodically to the cluster head. The cluster based routing protocols are initialized by the Low Energy Adaptive Clustering Hierarchy protocol (LEACH) [18]. LEACH is a routing protocol that collects and sends data to the centralized access point known as the base station. LEACH protocol aims at increasing the network life-time, decreasing the energy dissipation of the sensor nodes and reducing the number of communication messages transmitted between the nodes. In the traditional LEACH protocol, the energy consumption is reduced by choosing a node that consumes less energy as the cluster head. A variant of LEACH protocol known as Optimization Low Energy Adaptive Clustering Hierarchy (O-LEACH) [19] finds an alternate solution to reduce the energy consumption of the sensor nodes. In the O-LEACH protocol, the cluster heads are chosen based on the residual energy of the nodes (i.e.) at each round the node whose energy value is greater than ten percent of the residual energy value of the remaining sensor nodes is elected as the cluster head. The simulation results of O-LEACH prove it to be efficient over other LEACH protocols. In this paper, Zigbee based Wireless Sensor Network with static nodes is considered to be the existing work. The total number of sensor nodes taken for simulation is 100 and the total number of clusters is 5. Each cluster has 20 sensor nodes. The sensor nodes are initialised with 5J as initial energy. The existing work simulations are based on OLEACH with little modifications. The performance of existing work is analyzed through the evaluation of average residual energy and throughput.

•

Residual energy of a node = (Initial energy of a node) – ((transmitting energy + receiving energy + data aggregation energy + computed average energy) of a node).

The proposed work follows the below mentioned flowchart: Start

Set up ‘k’ sensor nodes in m*m topology, ‘l’ no: of clusters and each cluster have ‘n’ sensor nodes.

Set initial energy, transmitting energy, receiving energy and data aggregation energy. Initialize r=1 and set total no: of rounds.

If ‘r’ < = total no: of rounds

No

Yes Configure the nodes

Calculate the distance between the nodes

For the calculated distance, set the energy level to the nodes.

Calculate the total energy that a node requires to communicate with its member nodes and then compute its average energy consumption.

B

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A

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Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500 Parameter

Value

Energy for transmission

0.7 J

Energy for reception

0.9 J

Energy for data aggregation

5 nJ/bit/signal

MAC protocol

IEEE 802.15.4

A

Add the computed average energy with the transmitting energy, receiving energy and data aggregation energy.

Residual energy of a node = (Initial energy of a node) – ((transmitting energy + receiving energy + data aggregation energy + computed average energy) of a node).

In the simulation, average residual energy and throughput are computed and analyzed by varying data rate from 50 kbps to 250 kbps, number of nodes from 20 to100 and range from 50m to150m. Also energy efficiency based on average residual energy is computed by varying the number of nodes from 20 to 100.

No

Residual energy of node ‘i’ > residual energy of member nodes ?

Node ‘i’ is the member of a cluster.

Yes Node ‘i’ is the cluster head.

B

Broadcast Node ‘i’ is the cluster head to its member nodes.

Fig. 1. NAM output of Zigbee based WDSN with 100 mobile nodes.

Schedule the member nodes of a cluster with their cluster head on a TDMA basis; establish communication between the nodes and increment ‘r’.

The average residual energy and throughput are calculated by varying one metric and making the remaining two to be constant. For instance, data rate and range are kept constant while calculating the average residual energy by varying the number of sensor nodes.

End

The proposed work is developed and analyzed through simulations. The simulations are done using ns2 and the performance metrics such as average residual energy and throughput are evaluated and analyzed. In the simulation scenario, the sensor nodes are deployed in 1000 x 1000 m2 region and the scenario is illustrated in Fig 1. The simulation parameters used for simulation are listed in Table I. TABLE I.

SIMULATION PARAMETERS

Parameter

Value

Number of nodes

20,40,60,80,100

Number of clusters

1,2,3,4,5

Number of nodes in each cluster

20

Initial Energy of a node

5J

Transmission range

50m to 150m

Speed of the node

2m/s

Simulation time

25s

AVERAGE RESIDUAL ENERGY (J)

SIMULATION AND RESULTS

3.8

3.7

3.6

3.5

3.4 50

100

150 DATARATE(kbps)

200

250

Fig. 2. Average Residual Energy (J) vs Data rate (kbps).

4.2 AVERAGE RESIDUAL ENERGY (J)

IV.

3.9

4

3.8

3.6

3.4

3.2 20

30

40

50

60 70 NO: OF NODES

80

90

100

Fig. 3. Average Residual Energy (J) vs No: of Nodes.

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4.2 4.1 4 3.9 3.8 3.7 3.6 3.5 50

60

70

80

90

100 110 RANGE(m)

120

130

140

150

Fig. 4. Average Residual Energy (J) vs Range (m).

Fig 2, Fig 3 and Fig 4 depict the variation of average residual energy by varying data rate, number of nodes and range respectively. From Fig 2, it is observed that, as the data rate increases from 50 kbps to 250 kbps, the average residual energy decreases. This is because as the number of packets transmitted per second increases, the average energy consumption increases and this leads to a decreased average residual energy. From Fig 3, it is observed that as the number of nodes is increased from 20 to 100, the average residual energy also increases. The reason behind the increase in the average residual energy is that as the number of nodes increases, the residual energy at each node keeps on accumulating leading to an increased average residual energy. From Fig 4, it is observed that, as the range increases from 50m to 150m, the average residual energy decreases due to the more scattered nature of nodes.

Fig 5, Fig 6 and Fig 7 illustrate the variation of Throughput with respect to data rate, number of nodes and range respectively. From Fig 5, it is observed that, as the data rate increases from 50 kbps to 250 kbps, the throughput also increases. When the data rate increases; the number of packets in transition increases. Therefore when the channel conditions are good, approximately all the packets in transition will be received by the end user. Hence the throughput increases with increase in data rate. From Fig 6, it is portrayed that, as the number of nodes increases from 20 to 100, the throughput increases. When the number of nodes increases; the chance of packet loss decreases as the number of neighboring nodes between the source and destination increases. Thus increasing the nodes lead to an efficient delivery of the packets; thereby increasing the throughput. From Fig 7, it is depicted that, as the range increases from 50m to 150m, the throughput decreases due to the distributed nature of nodes. 85 E N E R G Y E F F I C I E N C Y (% )

AVERAGE RESIDUAL ENERGY (J)

4.3

80

75

70

65 20

30

40

50

60 70 NO: OF NODES

80

90

100

250

THROUGHPUT(kbps)

Fig. 8. Energy Efficiency (%) vs No: of nodes. 200

150

100

50

0 50

100

150 DATARATE(kbps)

200

250

Fig. 5. Throughput (kbps) vs Data rate (kbps).

The simulation results of existing and proposed work are compared when the number of nodes is 100 and the values are tabulated in Table II.

240 220 THROUGHPUT(kbps)

Fig 8 depicts the variation of energy efficiency with respect to the number of nodes. From the above figure, it is observed that as the number of nodes is varied from 20 to 100; the energy efficiency increases. The energy efficiency is calculated as the percentage of ratio of average residual energy and initial energy. Since the average residual energy increases when the number of nodes increases; the energy efficiency also increases.

200 180

TABLE II.

160

COMPARISON OF SIMULATION RESULTS FOR 100 NODES

140

Parameter

120 100 20

30

40

50

60 70 NO: OF NODES

80

90

100

Fig. 6. Throughput (kbps) vs No: of Nodes.

Throughput (kbps) Energy efficiency (%)

250

THROUGHPUT(kbps)

Average residual energy (J)

Existing work value

Proposed work value

3.88

4.07

147.68

226.57

77.6

81.4

200

150

From the simulation results, it is observed that the proposed work exhibits good performance even when the nodes are made to be dynamic in a Zigbee based Wireless Sensor Network.

100

50

0 50

60

70

80

90

100 110 RANGE (m)

120

130

140

150

Fig. 7. Throughput (kbps) vs Range (m).

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V.

CONCLUSION

In the recent years, many clustering schemes have been introduced for Zigbee based Wireless Sensor Network with static nodes. In this paper, energy efficient clustering protocol for a Zigbee based Wireless Dynamic Sensor Network is developed with mobile nodes. The outcomes of the simulation results depict that the proposed work performs well even when the network is dynamic thereby optimizing the average residual energy and throughput. REFERENCES [1]

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R.Maria Brigitta received her B.Tech degree (Gold Medallist) in Electronics and Communication Engineering from Manakula Vinayagar Institute of Technology affiliated to Pondicherry University, India in 2015. Currently she is pursuing her M.Tech degree in Electronics and Communication Engineering in Department of Electronics Engineering, School of Engineering and Technology, Pondicherry Central University, Pondicherry, India. Her area of interest includes Wireless Sensor Network and Zigbee network. P. Samundiswary received her B.Tech degree and M.Tech degree in Electronics and Communication Engineering from Pondicherry Engineering College affiliated to Pondicherry University, Pondicherry, India in 1997 and 2003 respectively. She received her Ph. D degree from Pondicherry Engineering College affiliated to Pondicherry University, Pondicherry, India in 2011. She has been working in teaching profession since 1998. Presently, she is working as Assistant Professor in the Department of Electronics Engineering, School of Engineering and Technology, Pondicherry Central University, India. She has nearly 18 years of teaching experience. She has published more than 70 papers in national and international conference proceedings and journals. She has co-authored a chapter of the book published by INTECH Publishers. She has been one of the authors of the book published by LAMBERT Academic Publishing. Her area of interest includes Wireless Communication and Networks, Wireless Security and Computer Networks. She will be available at [email protected].

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Prediction of Spacecraft Position by Particle Filter based GPS/INS integrated system Vijayanandh R [1], Raj Kumar G [2] [1], [2]

– Assistant Professor, Department of Aeronautical Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India E-mail: [email protected]

Abstract—Estimation of spacecraft location is an evergreen interesting research area and an avionics systems involves for predicting the spacecraft attitude determination like Inertial Navigation System (INS), Global Positioning System (GPS) is also a difficult concept to understand. Recent technological advances in both GPS and INS to providing the position and attitude of moving platforms for numerous positioning and navigation applications, GPS/INS integrated system is a better way to solve their internal drawbacks in the case of more accuracy based navigational and positioning estimation i.e., aerospace problems. To make this integration process as possible, some methods are available which are Kalman Filter, Unscented Kalman Filter, Neural based integration approach and Particle Filter etc. Among these approaches, only few can be able to solve non-linear based practical problems such as spacecraft attitude prediction, tracking of moving vehicles, etc. In this paper a new spacecraft attitude prediction method using particle filter algorithm is derived, based on sequential Monte Carlo simulation; the particle filter roughly represents the probability distribution of the state vector with random samples. Through some standard theoretical approach as well as simulation analysis, this PF algorithm has shown that good prediction performance and also has better healthiness to the system model and noise than the other traditional prediction integration techniques. Keywords-Attitude, Global Positioning System, Inertial Navigation System, Particle Filter, Spacecraft

I.

INTRODUCTION

A spacecraft’s attitude can be theoretically explained by a variety of data, including constrained (redundant) data such as the attitude orientation matrix and the unit quaternion, and unconstrained (minimal) data such as the Rodrigues parameters, the Euler angles and the modified Rodrigues parameters (MRPs). Constrained attitude data are singularity-free but lead to constrained data estimation problems that will have to be solving directly or circumvented in attitude estimators. The estimation of spacecraft attitude and its problem can be able to solve by using avionics systems such as navigation system, positioning system, etc. In general, the attitude of any moving object can be effectively found by positioning and navigation systems [1]. A. Inertial Navigation System Inertial navigation system (INS) is a system based on the concept of dead-reckoning navigation in which the components in the aircraft determine its acceleration and by successive integration, velocity and displacement are obtained.

Senthil Kumar M [3], Samyuktha S [4] [3]

– Assistant Professor (SRG), [4] – BE Student, Department of Aeronautical Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India Integrating the output from an accelerometer sensor gives speed and integrating speed provides distance traveled. The gyro sensor provides information regarding the angular rate of the spacecraft and acceleration is measured using the accelerometers. The system is entirely self-contained and can be used on earth, under the sea as well as space. Inertial navigation is one of the most versatile forms, which is used for automatic spacecraft navigation. It uses accelerometers and gyroscopes to measure the state of the motion of the moving vehicle. By knowing vehicle’s starting position and the changes in its direction and speed, one can keep a track of the vehicle’s present position. The Early inertial system used complex general mechanical gimbal structures and gyros. Nowadays, the gimbals are replaced by a computation scheme, which uses the gyros information of the vehicle angles to evaluate where the vehicle axes are with respect to the chosen coordinates and these computations need a good workstation. In INS, the inertial sensors provide the necessary signals for an automatic navigation [2]. B. Global Positioning System Global Positioning System (GPS) is a navigation system operated by using satellites in space. Two satellite assisted navigation systems are currently operating. The NAVSTAR Global Positioning System is a collection of 24 satellites operated by US Department of Defence but available for civil use. They are located at an altitude of 20,200 km in 6 different orbits, each inclined at 55 degrees to the equator. The basic principle used here is that of calculating the distance from the processing duration of radio energy. With very accurate timing the range of the receiver (the spacecraft) from the transmitter (the satellite) can be established with minimal error. One of the most important features of GPS is that it has a facility for degrading the accuracy for unauthorized users. The major advantage of GPS is that the accuracy does not degrade with time due to small errors like INS [3]. II. PROBLEMS IN SPACECRAFT ATTITUDE DETERMINATION Problem identification and its rectification method play a major role in every process, because the resulting procedure of all innovative ideas depends on the understanding of the problem identification. Problem identification helps to guide the process in the proper way and also uses to solve the problem in an efficient manner. In this paper, deals attitude determination of spacecraft by avionics systems with a low-level probability of failures [4].The errors in avionics systems are listed below for the purpose of managing these errors with the help of integration

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techniques to predict a spacecraft position with a high probability of success. A. Errors in INS Most INS instrument errors are accredited to the inertial sensors. Those errors are the residual errors exhibited by the installed instruments (gyro, accelerometer) following calibration of INS. The dominant error sources are Alignment errors, Accelerometer bias or offset, Accelerometer Scale factor error, Non-Orthogonality (Uncertainty and misalignment in the axes of the gyro and accelerometer), Gyro drift or bias due to temperature changes, Gyro scale factor error and Random Noise in measurement. All inertial navigation systems affect from integration drift: fewer amounts of mistakes in the readings of acceleration and angular velocity are joined into the progressively higher amount of mistakes in velocity, which are combined into still superior mistakes in position. Accordingly, inertial navigation is usually used to combine with other navigation systems, providing a higher degree of attitude information than any single system [5]. B. Errors in GPS Though the accuracy of GPS does not degrade with time, it also has certain inherent errors. There are six major errors in GPS. The orbit parameters of the satellite are referred to as ephemeris. Satellite errors which are relevant to the position are directly inducing fitting errors. The master control ground station (MCS) monitors and controls satellites orbital position to ensure the range mistakes resulting from ephemeris inaccuracies stay within the specified limit of 0.5 meters. Transmission timing and range measurement is dependent on the satellite clock. Clock errors are corrected by the MCS. The ionospheric error is also known as atmospheric propagation errors. The signals passing down through the ionosphere are slowed by the small amount of refraction that occurs. The error will vary with time of day, year and elevation of the satellite but it should not be more than 4 meters. Multipath error is caused by reflected signals from the surfaces near the receiver that can either join with or be misguided for the signal that follows the straight line path from the satellite. Multipath is not easy to detect and sometimes hard to evade, however, the effects can be mitigated to a certain coverage by special satellite dish design and/or enhanced receiver software. Any range error resulting from multipath propagation should not exceed 0.5 meters. Orbital perturbation errors occur due to distortion in the orbit. The major problem is caused by the earth’s equatorial bulge; however solar wind, the gravitational pull of celestial bodies and other parameters affect the GPS system. Corrections for GPS orbital perturbations are defined and televise at least once per day, via the satellites to the users, along with clock correction data. Instrument or receiver errors are the errors that arise in GPS receiver due to electrical noise as well as in time measurement and range/position computation. Range errors of 1 meter are possible due to such causes [6]. C. Problems in spacecraft A spacecraft is a machine which can able to fly in the lowlevel oxygen region. The process of navigating the spacecraft is

totally different from normal aircraft that is Thrust Vector Control (TVC) mechanism, TVC is the one which used to control the maneuvering of a spacecraft with the effect of exhaust velocity. So the attitude determination of a spacecraft take place major role in the aerospace industries, apart from the guidance and control of a spacecraft, the space environment also affects the performance of a spacecraft to make its attitude determination as complicated. The reasons for spacecraft failures are, 1. Van Allen radiation belt 2. Temperature environment problems 3. Collision with space debris 4. Payload relevant problems 5. Control mechanism problems To overcome these problems, integration of GPS and INS by PF would be a better solution. III.

GPS/INS INTEGRATION

The navigation systems are classified into various types; each and every system has its own advantage and disadvantages, which are distinctive to it. The INS is independent, but the error increases severely with raised in time. It needs to be calibrated and corrected periodically; else the accuracy will degrade to objectionable limits. To calibrate and correct the errors in INS, another navigation system is needed which gives the navigation parameter in an accurate way. The GPS system is a more accurate method to integrate with INS because it accuracy does not vary with time but still it affect from its own disadvantage and errors which are mentioned earlier. The Hybrid Navigation System has been designed to exploit the advantages of both the systems while overcoming their inherent drawbacks. The INS can be calibrated using the GPS and the values obtained through GPS can be compared or cross checked with that of the values of INS, therefore the accuracy is assured in the navigation parameters are obtained. The process of obtaining a hybrid navigation system by combining INS and GPS is commonly known as Integration of INS and GPS. The amalgamation of GPS data with Inertial Measurement Units (IMUs) has become a standard technique for location and attitude determination of a spacecraft. The Kalman filter (KF) is widely used in practice to blend GPS measurements with IMU data, but it has one well-known drawback. If non-linear based practical problems and also attitude errors are not within the linear region, then the filter divergence may occur. This is a problem for an integrated GPS/INS since; even though the position is fit known, attitude and IMU calibration parameters may not be well known a prior. Particle Filter, essentially provide derivative-free higher-order predictions by approximating a Gaussian distribution rather than resembling an arbitrary nonlinear function as the KF does. They can provide more accurate results than a KF, particularly when precise initial condition results are not well known so, this paper aims to utilize Particle Filter algorithm to get a better solution for INS/GPS Integration [7]. Figure 1 shows the working flow of GPS/INS integration of a spacecraft.

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Figure 1. GPS and INS integration block diagram

A. Particle Filter The Particle Filter Method is a Monte Carlo appraoch for the solution of the attitude prediction problem. The particle filter is moreover identified as the bootstrap filter, condensation algorithm, interacting particle approximations and survival of the fittest. The main step is to characterize the required posterior density function (pdf) by a set of random samples (particles) with associated weights, and to calculate the estimates based on these samples and weights. A particle filter uses the Bayesian approach for state estimation of a spacecraft but with a difference that the pdfs are considered to be available in sampled form. As mentioned above for Bayesian approach, it is a recursive filter consisting of two stages: prediction and update. The prediction step involves the system model to predict the status pdf frontward from one measurement time to the after that. Since the state is usually subject to unknown disturbances (modeled as random noise) prediction usually translates, deforms and spreads the state pdf [8]. The basic Particle Filter algorithm mainly consists of two steps, the first one is sequential importance sampling step and the second one is selection step. 1. Sequential importance sampling step • Uses Sequential Monte Carlo simulation. • For each particle at time t, we sample from the transition priors • For each particle, we then evaluate and normalize the importance weights 2. Selection Step • Multiply or discard particles with respect to high or low importance weights w (i)t to obtain N particles. • This selection step is what allows us to track moving objects efficiently.

Figure 2. Basic Particle Filter Algorithm – Schematic

B. Estimation of spacecraft position Assessment process of spacecraft attitude contains the two techniques: 1) estimation of a spacecraft orientation from body measurements and known reference details, such as line-

of-sight data to known observed stars, and 2) filtering of noisy measurements. This assessment process step is achieved with the help of incorporating the data with models, which itself can be done a number of dissimilar ways. One way is to use a threeaxis rate combining gyro sensors propagated with kinematics model. However, the rates measured by gyro sensors drift with respect to time. Therefore, the attitude orientation position vector is usually appended by three states to evaluate this drifting drawback, this leads to a complementary technique, where the gyro sensors are used to filter the noisy body data and the measurements are used to determine the drift intrinsic in the gyro sensors. Another way contains integrating the kinematics model with a non-linear model for the angular rate. However, even a detailed non-linear model, such as Euler’s rotational equations, will have intrinsic errors. For example, the inertia matrix may not be fit known; this is balanced in filter designs by using process noise, which leads to the classic “tuning” trouble in the filter approach. In this paper, the spacecraft position prediction follows the second process with the help of Particle Filter approach. In order to predict the spacecraft state determination non-linear problem and also to implement the integration Particle filter requires mathematical models for the INS error. In this integrated system, the following ψ angle error model is applied, (1) , = −ω , X δP, + δV, − 2 , + , = , X δV, + f, X φ, + (2) C , ∇ = − + C ε (3) , , , , , δV, and δP, are the attitude, velocity and Where position errors in local frame respectively, ∇ is the accelerometer error, ε is the gyroscope error. Eq. (3) can be described with state space equation; it is the system equations of the filter, ] (4) = [ = [∇ ∇ ∇ ] (5) Where is navigation error state vector, is the sensor state error vector [1, 9]. The updated values of navigation state vector and sensor state vector of the spacecraft is fully depends upon the position matrix, weight of the particles and previous navigation and sensors data. Measurement model is given by = ℎ + (6) IV. PARTICLE FILTER BASED INTEGRATION Nowadays GPS/INS integrated navigation system is being given a large amount of notice, it is broadly used in several positioning and navigation fields. Particle based state computing, one of the technologies of PF, which is an useful tool for solving nonlinear problems i.e., spacecraft attitude estimation, that involve mapping input data to output data without having any relevant knowledge about the mathematical process involved. A Particle Filter is derived for incorporating GPS data with inertial sensor measurements from gyros and accelerometers to determine both the position and the attitude of a spacecraft. Particle filters uses a carefully selected set of sample particles to more accurately plot the probability distribution than the linearization of the Kalman filter, leading

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to faster convergence from imprecise initial conditions in attitude assessment troubles, PF uses normalized particles weight and time updates to correct system states modification and to limit the modifications in navigation solutions. However, only when the system is nonlinear and measurement models are properly defined and the noise statistics for the process are completely known, Particle Filter can optimally estimate a system’s attitude. Without normalized particles weight and time updates, Particle filter’s prediction diverges; therefore, the concept of an integrated GPS/INS navigation system may disgrace quickly when GPS signals are unavailable. By using the uniform attitude pdf as the initial attitude distribution and using a steadily lessening data variance in the calculation of the importance weights, the particle filter based attitude estimator possesses worldwide convergence properties. The filter formulation is based on regular inertial navigation equations. The universal attitude parameterization is given by a quaternion, while a generalized three-dimensional attitude illustration is used to define the local attitude error [10]. A multiplicative quaternion-error approach is used to assurance that quaternion normalization is maintained in the filter. A. Particle Generation The particle filter algorithm starts with generating particles set which are nearby the spacecraft. Unlike the Unextended Kalman Filter, where the sigma particles are chosen in a deterministic way, the PF spreads the particles in a stochastic way. The first step is to predict the next particle set. This is achieved by adding process noise to the state and evolving it using the plant. This set will contain good and bad estimates of the spacecraft attitude position. Since only good estimates are useful for estimating the spacecraft state, the particle set has to be resampled. The PF does not try to estimate the probability p xk/z1: k instead it estimates the conditional probability of the whole trajectory of the state p xk/z1:k, where x1:k={1,…, xk}. The price that must be paid for this flexibility is computational. However this approach increasing computational power, these integrating approaches are already used in real-time practical applications appearing in fields as diverse as chemical engineering, computer vision, financial econometrics, spacecraft tracking and robotics. The initial particle set with size N is generated by adding process noise to the first target position [11]. B. Resampling algorithm The simplest method of resampling is to select each particle with a probability equal to its weight. The resampling algorithm starts when a measurement is received. The first step is to assign a weight to each particle using the measurement. The weight of the particle is given by: =

(

)

(7)

Where k is constant and defined as Mahalanob is distance between the particle and measurement, the description of Mahalanob is deals with distance between two points x=(x1, x2,.., xn)T and y=(y1, y2,.., yn)T in n-dimensional space is given by: ( , ) = ( − ) ( − ) (8)

Where S is the covariance matrix, the exponential term is divided by a normalizing constant determined by the state vector size D. A stratified resampling scheme was employed to resample the particle set. First, the weights are normalized by dividing each weight by the sum of all weights. The cumulative sum of the particle weights is calculated. A sequence of sorted N random numbers uniformly distributed in is selected. Finally, the number of the sorted random numbers that appear in each interval of the cumulative sum represents the number of copies of this particular particle which are going to be propagated forward to the next stage. Intuitively, if a particle has a small weight, the equivalent cumulative sum interval is small and therefore, there is only a small chance that any of the random numbers would appear in it; in contrast, if the weight is large then many random numbers are going to be found in it and thus many duplicates of that particle are going to survive [12].

Figure 3. Particle Filter working flow chart for spacecraft attitude prediction

With the inclusion of the spacecraft non-linear mathematical model and Euler’s rotational equations the internal procedures involves in particle filter explains below, (9) xk = f k (xk-1, vk-1) where the subscript k = 1, 2, …, tells a time instant tk in a practical problem [5, 13]. The vector x ∈ Rnx is called the spacecraft state vector and includes the variable functions to be dynamically determined. This vector advances in accord with the state estimation model given by equation (9), where f is, in the common case, a practical function of the state variables x and of the state noise vector v ∈ Rnv. Consider also that measurements zk ∈ Rnz are available at tk, k = 1, 2, …. The measurements are linked to the state variables x through the common, possibly non-linear, function h in the form (10) zk = hk (xk, nk) where n ∈Rnn is the measurement noise, Equation (10) is referred to as the observation (measurement) model. The state estimation problem aims at providing information about xk based on the spacecraft state estimation model (10) and on the measurements z1:k = {zi, i =1, ...., k} given by the observation model (10). The evolution-observation model given by equations (9, 10) are based on the following assumptions [5, 14]: (i) The sequence xk for k = 1, 2, …, is a Markovian process, that is, (11) π(xk | x0, x1, ...., xk-1) = π (xk | xk-1) (ii) The sequence zk for k = 1, 2, …, is a Markovian process with respect to the history of xk , that is, (12) π(zk | x0, x1, ...., xk-1) = π (zk | xk)

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(iii) The sequence xk depends on the previous observations only through its own history, that is, (13) π(xk | xk-1, z1:k-1) = π (xk | xk-1) where π (a | b) denotes the conditional probability of a when b is given. In addition, for the estimation-observation model given by equations (9, 10) it is assumed that for ‘i’ is not equal to ‘j’ the noise vectors vi and vj , as well as ni and nj , are mutually independent and also mutually independent of the initial state x0. The vectors vi and nj are also mutually independent for all i and j. Different problems can be considered with the above estimation-observation model, namely: • The prediction problem, concerned with the determination of π ( xk | z1:k-1 ) ; • The update problem, concerned with the determination of π ( xk | z1:k ) ; • The fixed-lag smoothing problem, concerned with the determination of π ( xk | z1:k+p ), where p ≥1 is the fixed lag; • The whole domain smoothing problem, concerned with the determination of π ( xk | z1:K ), where z1:K = {zi,i =1, ...., K} is the complete sequence of measurements [5, 15]. By assuming that π ( x0 | z0 ) = π ( x0 ) is available, the posterior probability density π ( xk | z1:k ) is then found with Bayesian filters in two process: prediction and update [5]. Given a prior p(x1), a transition prior p (xt|xt-1) and a likelihood p (yt|xt), the algorithm is as follows: Initialization, t=1 for i=1,……….,N, sample (x1( i ))~p(x1 ) and set t=2. Importance sampling step for i=1,…….,N sample xpt( i ) ~p(xpt( i )|xt-1( i )) and set xp1:t( i ) =( xt( i ) , x1:t-1( i ) ). for i=1,……, N, Evaluate Importance weights wt ~p(yt|xt( i ) ) Normalize the importance weights. Selection Step Resample with replacement N particles (xi:t( i ); i=1,…….,N) from the set ( xp1:t( i ) ; i= 1,……., N) according to the normalized importance weights. Set t = t+1 and go to step 2 [16]. V.

direction. It's starting position is 2130 SW, NL 1104’36’’ and EL 7700’6’’. Steps involved in a new GPS/INS integration filtering algorithms are, Initialization - Get the spacecraft data from system 1 (INS) Update-Update the spacecraft data by using System 2 (GPS) Integration – Integrate the system1 and system 2. Implementation – implement the Particle Filter Figure 4 provides information about the state of the moving vehicle. The state of the vehicle varies depends on upon the time step value. The true state value and the particle filter predicted state value almost located the same location because of the nonlinear mathematical model consideration and particle filter state estimation technique.

Figure 4. Path prediction of moving object by PF

Figure 5 gives a plot between spacecraft state error versus time, the error of the spacecraft state gets reduced depending upon the time variation. The error value minimum at the end of the analysis and maximum at the start of the analysis,this analysis has been carried out about 400 sec. The main reason behind this spacecarft state estimation is selection of particles and its weight.

RESULT AND DISCUSSION

In the proposed loosely-coupled GPS/INS navigation system, PF estimates the INS measurement mistakes, spacecraft attitude mistake, and provides accurate navigation solutions while GPS signals are available. When INS information shortage, the integrated model is established and its in-house arrangement is tuned to imitate the present spacecraft attitude with the help of GPS. During periods of GPS signal blockage, the discussed Particle-based state computing algorithm works in the prediction of moving objects data to estimate the spacecraft position changes based on the INS velocity and azimuth information. In order to analyze the correlative algorithm of the planned GPS/INS integrated attitude estimation system, some simulation analysis is given through comparing a normal GPS and INS results with GPS/INS integrated results. During the simulation process, the reference spacecraft model typical flight is horizontal, flying to the west

Figure 5. Error estimation of spacecraft by PF

Figure 6 gives the variation of true position and particle filter prediction of a spacecraft state, the particle filter prediction almost similar to true state at the initial stage but it changed at the end of the stage because of problems in selection of particles, necessary action for this problem is to normalize the particles and resample once again.

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[4]

[5]

[6] Figure 6. Path prediction of spacecraft by PF

Figure 7 shows the comparison of RMS horizontal error and RMS vertical error and aslo it infer that both the errors are less in GPS/INS integration and more in GPS and INS systems. The spacecraft state estimation with the help of GPS/INS integration performed more accuracy compared to Global Positioning System and Inertial Navigation system.

[7]

[8]

[9]

[10]

[11]

Figure 7. GPS/INS integration by PF

VI.

CONCLUSION

A sampling and Re-sampling algorithm are used in the particle filtering for GPS/INS integration for attitude determination. This paper provides the better solution for spacecraft attitude problem so that the failure of spacecraft gradually decreases, it may chance to increase the usage of spacecraft with a low probability of failures because the estimation of spacecraft attitude based on particle filter fully depends on the particles which are high weight and disturbed by the main object (spacecraft). Generally in the case of nonlinear navigational problems, one or more integration approaches are used to predict the state of the spacecraft but in this proposed particle filter method is a single better approach can able to solve non-linear critical problems than the other conventional approaches and also it gives a significant improvement in some performance, such as accuracy, low-level failure probability. Finally, the simulation results of spacecraft state prediction and low-level error determination clearly shows that determination of spacecraft attitude by particle filter based GPS/INS integrated system is the best approach because of its practical consideration and estimation techniques involves. REFERENCES [1]

[2]

[12]

YUE Xiao-kui, YUAN Jian-ping, “Neural Network-based GPS/INS Integrated System for Spacecraft Attitude Determination”, CHINESE JOURNAL OF AERONAUTICS, Vol.19 No. 3. August 2006, page no 234 – 238. Yang Cheng¤ and John L. Crassidis, “Particle Filtering for Sequential Spacecraft Attitude Estimation”, American Institute of Aeronautics and Astronautics, page no. 1 – 18.

[13]

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[15]

[16]

Andrew G Dempster, “Satellite Navigation: New Signals, New Challenges”, School of Surveying and Spatial Information Systems, University of New South Wales, Sydney 2052. Arulampalam, Maskell, Gordon, Clapp: A Tutorial on Particle Filters for on-line Non-linear / NonGaussian Bayesian Tracking, IEEE Transactions on Signal Processing, Vol. 50, 2002 “Kalman and Particle filters” H. R. B. Orlande1, M. J. Colaço1, G. S. Dulikravich, F. L. V. Vianna, W. B. da Silva, H. M. da Fonseca and O. Fudym, page no 1 – 39. “Particle Filters for Positioning, Navigation, and Tracking” Fredrik Gunnarsson, Niclas Bergman, Urban Forssell, Jonas Jansson, Rickard, Per-Johan, Final version for IEEE Transactions on Signal Processing. Special issue on Monte Carlo methods for statistical signal processing. A Novel Data Fusion Approach in an Integrated GPS/INS System Using Adaptive Fuzzy Particle Filter, Ali Asadian and Behzad Moshiri University of Tehran, Tehran, Ali Khaki Sedigh, University of K.N.T,Tehran, Iran S. Arulampalam ,S. Maskell ,N. Gordon and T. Clapp, “A tutorial on particle filters for on-line non-linear/non-gaussian bayesian tracking”. IEEE Trans. Sig. Proc., vol 50, no 2, February 2002, pp. 174-188. “Sequential monte carlo methods for multiple target tracking and data fusion,” , C. Hue, J. P. Le Cadre and P. Pérez, IEEE Trans. Sig. Proc., vol 50, no 2, February 2002, pp. 309-325. “Mathematical Modeling and Stabilization of Large Flexible Spacecraft Subject To Orbital Perturbation”, S. S. LIM and N. U. AHMED, Canada, (Received December 1988; accepted, for publication March 1989), page no 1487 – 1504, volume no 12. A. Asadian, B. Moshiri and A. Khaki Sedigh, “Nonlinear optimization in an integrated GPS/INS system in critical situations using particle filters”, in Proc ICEE2005 Conf., Zanjan, Iran, May 2005, pp. 93-99. “A Practical Method for Implementing an Attitude and Heading Reference System”, Rodrigo Munguía and Antoni Grau, Received 24 Jun 2013; Accepted 29 Jan 2014, DOI: 10.5772/58463, International Journal of Advanced Robotic Systems. “Integration of INS and GPS system using Particle Filter based on Particle Swarm Optimization”, Meriem JGOUTA and Benayad NSIRI, International Journal Of Circuits, Systems And Signal Processing, Volume 9, 2015, ISSN: 1998- 4464, page no 461 – 466. GPS/INS Integration: A Performance Sensitivity Analysis, Wang Jin-ling, Lee H K, Rizos C, School of Surveying and Spatial Information Systems, The University of New South Wales, Sydney, Australia. “Rao-Blackwellized Particle Filtering for 6-DOF Estimation of Attitude and Position via GPS and Inertial Sensors”, Paul Vernaza and Daniel D. Lee, GRASP Lab, University of Pennsylvania, Philadelphia, PA 19104. “A Survey of Nonlinear Attitude Estimation Methods”, John L. Crassidis∗ and Yang Cheng‡, University at Buffalo, State University of New York, Amherst, F. Landis Markley†, NASA Goddard Space Flight Center, Greenbelt. AUTHORS PROFILE

R. Vijayanandh, Author: Completed BE in Aeronautical Engineering from Rajalaskhmi Engineering College, Chennai. Completed his M.E in Avionics from Madras Institute of Technology, Chennai. He has three years of teaching and research experience in Kumaraguru College of Technology, Coimbatore. G. Raj Kumar, Co-Author: Completed his BE in Aeronautical Engineering from Park College of Engineering and Technology, Coimbatore. Completed his M.E in Aeronautical Engineering from Dhanalakshmi Srinivasan College of Engineering and Technology, Chennai. He has three years of teaching and research experience in Kumaraguru College of Technology, Coimbatore. M. Senthil Kumar, Co-Author: Completed his B.Tech in Mechanical Engineering from Pondicherry Engineering College, Pondicherry Completed his M.E in Aeronautical Engineering from Park College of Engineering and Technology, Coimbatore. Pursuing his PhD from Coimbatore Institute of Technology, Anna University-Chennai. He has ten years of teaching and research experience in Kumaraguru College of Technology, Coimbatore. S. Samyuktha, Co-Author: Pursuing her Under Graduate degree in Aeronautical engineering from Kumaraguru College of Technology, Coimbatore, Tamil Nadu

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Dynamic Application Centric Resource Provisioning Algorithm for Wireless Broadband Interworking Network S. Kokila Department of Electronics and Communication Engineering Pondicherry Engineering College Puducherry, India [email protected] Abstract—Allocating users to the Access Technology with a suitable channel and bandwidth conditions is the key issue of Radio Resource Management (RRM) in an interworking network, to provide guaranteed Quality of Service (QoS). IP Multimedia Subsystem (IMS) provide interworking networks with a common set of control and routing functions, to converge heterogeneous protocols, functional entities and applications. In this paper, a Dynamic Application Centric Resources Provisioning Algorithm (DAC-RP) for a Ultra Mobile Broadband (UMB)-Worldwide Interoperability for Microwave Access (WiMAX)–Wireless Local Area Network (WLAN)hybrid interworking network, over a novel Intelligent Internet Protocol (IIP) architecture is proposed. The proposed IIP is a unified architecture, obtained by merging IMS Call Session Control Functions (CSCFs), Application services, enhanced IMS emergency and centralized services, under a common layer. It provides the users with the access to common applications across the divergent interworking network and reduces the signalling overheads through a fused level of interaction. With the added features of the IIP, the proposed DAC- RP scheme provides a dedicated set of channels for real-time and non-real-time applications. The performance metric for the RT and NRT applications are simulated for IIP based UMB-WiMAX-WLAN hybrid interworking network developed using OPNET 14.5 and compared with the scenario using existing IMS to prove the competence of the proposed IIP. KeywordsRadio Resource Management; Resource provisioning; Quality of Service; broadband wireless network; Complete partitioning; Interworking network; Call Control layer; IP Multimedia Subsystem

I.

INTRODUCTION

Integrating diverse radio access networks with heterogeneous capability over a common platform is the vital solution to meet the soaring requirement of multimedia and interactive applications of the developing broadband wireless access technology. The promising development of Fourth Generation (4G) and beyond technologies employ the overlapping of the existing Third Generation Partnership Project (3GPP) and Non 3GPP networks with a diverse heterogeneous characteristic [1]. The complementary characteristics of the Wireless Local Area Network (WLAN) offering high bandwidth with less coverage; the Quality of Service (QoS) facilitated larger coverage region but with

G. Sivaradje Department of Electronics and Communication Engineering Pondicherry Engineering College Puducherry, India [email protected]

limited mobility presented by Worldwide interoperability for Microwave Access (WiMAX); and Ultra Mobile Broadband (UMB) with greater flexibility and interoperability with both 3GPP and non 3GPP network, provide a platform to integrate these technologies to achieve optimization of the network functionalities. The provision of interworking heterogeneous network topologies is conceptually a prominent notion; however, it is certainly a challenge to the network designer in terms of improving the overall system capacity and user perceived QoS through a more intelligent and flexible network Radio Resource Management (RRM) strategy. As the demand for high data rate for best effort application increases, the need for proficient RRM design is gaining importance, in particular for the overlapping heterogeneous technologies, due to the dynamic protocols, and control functions. The flexibility of all-IP network can be fully exploited by IP Multimedia Subsystem (IMS), designed to allow the convergence of the existing 3GPP and Non 3GPP technologies [2]. Integrating technologies over IMS bestow with support for multi access and multi protocol, secure, reliable and trusted multi service communication for both real time and non real time, over a common QoS enabled core network. With the emerging advancement in IMS, the challenges in the evolution toward the next generation interworking network, such as intelligent radio resource sharing among heterogeneous nodes; distributed control technique; increased dynamics of inter-cell functionalities involving mobility and AAA, etc., could be meet. With all these issues of interworking network, RRM is the key research aspect to maximize the resource utilization which involves a centralized method to balance the load, congestion control, admission control, Initial RAT selection, and allocating resource based on QoS requirements [3]. Diverse RRM techniques for Resource provisioning in a heterogeneous wireless network are developed in current studies. Connection admission control based on the signal to Interference ratio and delay metric of the network are considered in [4]. Simplified Dynamic Hierarchy Resource Management (SDHRM) algorithm composed of predictionbased network- level resource allocation and connection level network selection is proposed in [5]. The SDHRM algorithm

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takes advantage of the tidal traffic to exploit the resources efficiently. Capacity and spectrum sharing algorithm for efficient Radio resource utilization for a mobile network were discussed in [6]. A Predictive QoS balancing and Predictive Load Balancing algorithm to achieve improved end user throughput is developed in [7]. A distributed multi – service resource allocation method is given in [8] were, each network employs a priority mechanism in order to give a higher priority on its resources to its subscribers on the home network than to the other users. [9] Employs a joint allocation algorithm using modified Newton method adopted to maximize the total system capacity by choosing the optimal bandwidth for the services and power allocation to that bandwidth. Several resource allocation algorithms were implemented for efficient radio resource utilization, among which channel (Bandwidth) allocation based on varied selection criterion forms an imperative CRRM technique. RRM methods in literature is classified into centralized and distributed, where a completely centralized solution of RRM does not consider the network’s QoS policies and increase the overhead by augmenting more decision criteria. On the other hand an absolute distributed RRM algorithm increases the restricted access of available resource, without taking into account the network metrics, but increases the QoS of user depending on the application profile. In this paper, a Dynamic Application Centric Resources Provisioning Algorithm (DAC-RP) algorithm (Extension of the work referred as Active Resource Provisioning (ARP) described for IMS architecture [10]) is proposed. It is based on partitioning the available channels, to select the suitable spectrum under the UMB-WiMAX-WLAN interworking RAT developed over the proposed IIP architecture, to serve the real time and non real time application profile. The hybrid UMBWiMAX-WLAN integration is done over the proposed Intelligent Internet Protocol (IIP) with additional emergency, centralized and application services, over converged IMS layer functionalities. The rest of the paper is organized as follows. Section II describes the features of UMB, WiMAX and WLAN networks, and the need for selection of the following networks for interworking. Section III explains the function of the IIP servers (modified IMS) and their functionalities to support the application with varied QoS and network level constraint. The Proposed Dynamic Application Centric Resources Provisioning Algorithm (DAC-RP) for allocating users with appropriate channel to serve the RT and NRT applications (both new and handoff), through complete bandwidth partitioning of the interworking networks with dynamic bounds is explained in section IV. The performance metrics for Voice over Internet Protocol (VoIP), File Transfer Protocol (FTP), and Email applications are obtained for the DAC- RP Algorithm implemented for IIP based UMB-WiMAX-WLAN interworking architecture simulated using OPNET 14.5 is discussed in section V. The performance of the DAC is validated through comparison of IIP based UMB-WiMAXWLAN network without RRM implementation. The encroachment of the developed IIP server architecture’s features is validated through obtaining the same for network interconnected over existing IMS [10]. The conclusions from

the proposed network IIP architecture and DAC-RP are given in section VI. II.

UMB-WIMAX-WLAN INTERWORKING ARCHITECTURE

The evolution towards merging the existing broadband (WLAN, WiMAX, WiBro), 3G (UMTS, HSDPA), 3GPP (LTE, LTE-A) and 3GPP2 (CDMA2000, EV-DO, UMB) technologies over a common platform, open new opportunities to provide voice, data and video traffic with increased throughput, speed, network capacity, and with decreased cost of service. The complementary characteristics of the existing technologies could provide a balance in the interworking network providing support for diverse application with varied features. In this paper, a novel hybrid interworking architecture of UMB-WiMAX-WLAN has been developed as shown in Fig.1. The choice of the networks is based on a primary common factor that the networks are of All-IP based network, and can be integrated to work under the common IMS architecture. The developed hybrid interworking architecture combines benefit of both loose and tight coupling, providing the users with the complete access to the radio resource of all interworking network without the need for complex element and protocol modifications. The proposed All- IP hybrid coupled architecture is integrated to form a coordinated system at the network layer, making the resource and end user routing efficient to support the QoS and QoE requirement.

Figure 1 Hybrid UMB-WiMAX-WLAN Interworking Architecture with proposed IIP servers

UMB developed by Qualcomm complement the 3G deployment through necessary features supporting both real time and best effort traffic with flawless mobility. UMB a part of 3GPP; developed as a successor of CDMA2000 EV- DO mobile technology standard [11] attains a fast data rate up to 275 Mbps in downlink and 75Mbps of uplink speed. The high data rate network is an optimized design, to efficiently support increased network capacity with enhanced user experience, to sustain the plethora of multimedia and broadband applications. The higher rates/ capacity and improved cell- edge performance features of the technology are obtained through the implementation of integrated techniques such as OFDMA, MIMO, SDMA, which has made UMB a center of the future

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mobile broadband application supporting devices. UMB with sophisticated control and signaling mechanisms, RRM, support for inter-technology handover and seamless operation, adaptive reverse link (RL) interference management and beamforming methods, the technology is likely to be the primary standard adopted for next generation mobile communications. The WiMAX standard is typically based on interoperability, which defines a service flow framework to support multiple QoS classes and delivers high-speed wireless broadband at a much lower cost [12]. The frequency range of a fixed standard WIMAX system covers from 2 to 11 GHz, while mobile standard cover below 6 GHz. However the most likely used frequency bands identified for WiMAX are, 2.3 GHz, 2.5 GHz, 3.5 GHz and 5.7 GHz. Depending on the frequency of operation used the system can be either Time Division Duplex or Frequency Division Duplex configuration. WiMAX can provide a theoretical transmission capacity of maximum upto 75 Mbps in 20 MHz bandwidth with coverage of 40 km, for fixed standard, but the typical value will be 20 to 30 Mbps. While with mobility, it can render upto 30 Mbps per subscriber, in a 10 MHz frequency spectrum, but with a practically available data rate of only 3- 5 Mbps. It employees OFDM modulation / demodulation technique at the physical layer and TDMA mechanism at the MAC layer, to provide improved capacity and effective utilization of the resource, through even distribution of bandwidth among several devices. With additional functions such as randomization, forward error correction (FEC), interleaving, and multiple antenna techniques, the WiMAX standard is a possible way to obtain better radio spectrum efficiency to meet the specified QoS requirements. WLAN is the standard developed to define the wireless connectivity of computer or device to a network which enable to send and receive data anywhere within the range of its access point. Several IEEE 802.11/WLAN standards were developed, from IEEE 802.11 ‘a’ to ‘n’, with unique functionalities, yet the selection is based on the particular requirements of the user. Each developed standard of IEEE 802.11 has its own range of frequency spectrum allocated and specific scheme of operation. IEEE 802.11a operates in the 5GHz band supporting a data rate of 54 Mbps with OFDM physical layer specification and 802.11b support upto 11 Mbps in the 2.4GHz band with DSSS modulation scheme. IEEE802.11g released in 2003, act as an extension to 802.11b standard by offering a data rate of 54 Mbps in 2.4GHz of the band. The ease of deployment and cost effectiveness due to the use of unlicensed frequency spectrum and varied functions of the standard had made WLAN gain popularity and has made it the most widely deployed technology for wireless internet applications. III.

PROPOSED IIP ARCHITECTURE

Unifying the available technological diversity is of the utmost important process to manage the spectrum conflict and demand to serve the emerging growth of the mobile applications and services. The support for multiplay action of voice, data and multimedia applications of the interworking heterogeneous network as a single IP based infrastructure,

need a consistent and robust services architecture. The novel Intelligent Internet Protocol (IIP) proposed can be described as a unified architecture with Media Transport/Connectivity layer, Control layer, and the service and application layer, as a common core. The key benefit of the IIP is that the layers of the IMS are merged, to provide common application, call control and transport functionality to multiple services, and users across multiple access networks, with effectively modifying the existing network essentials and access network, thus reducing the new element deployment cost. A. Media Transport/ Connectivity Layer This layer affords the convergence of all networks (fixed telephony, wireless, GSM, 3G, 3GPP, Non- 3GPP, etc.) to the common IP-based IMS core network [13]. Without the regard of the network being used, this layer is responsible for the data transfer, initiating and terminating SIP sessions, providing bearer services, convert analog to digital format to support real time and SIP protocol. The transport layer also provide the user equipment connectivity and IMS interaction, liberated of the underlying radio access technology. B. Call Control Layer The Control layer provide communication between device and service through the CSCF servers, (P-CSCF, I-CSCF, and S–CSCF), SIP and the HSS servers. It enables the registration and routing of the signalling message, control of traffic between the control and the service layer. The core network elements of the call control layer facilitate the QoS guarantee for the network by combined performance of the servers associated. P-CSCF- Proxy- CSCF, the first point of attachment to the core of the IIP architecture is associate the secure establishment of the session with the clients based on the SIP messages. The proxy CSCF address is discovered by the subscriber device, which could be used to find the location of the device based on the P-CSCF to which it is connected. It communicates with the I-CSCF to find the nature of call originated, if it could be from the device’s home network, of from the visitor network of the subscriber. It performs the ICSCF identification, monitoring the movement of subscribers, maintains the SIP signaling procedure, sustains the QoS level by assigning appropriate bearer resource, and affords security. I-CSCF; Interrogating- CSCF interconnects and routes the signaling messages among the IMS province securely. Domain Name System (DNS) of the subscriber node domain specifies the IP adder of the I-CSCF. It sends queries to the HSS server to retrieve the S-CSCF’s address to perform SIP registration, to locate the subscriber location and to forward signalling request and response. S-CSCF; Session-CSCF, is the central component that enables the coordinated interaction of all control layer and signaling entities. Based on the user profile fetched and depending on the triggers specified by the client, S-CSCF differentiates the specific type of SIP message to be routed to the prescribed Application Server (AS). It carries out routing services to PSTN, accompanied with other Call control layer entities such as BGCF, MGW, and MGCP. Located in the

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home network, it inspects every signaling message, depending on the policy enforced by the serving network.

videoconferencing, messaging, community services, presence, and content sharing.

The Home Subscriber Server (HSS) is the main element within the core of IIP architecture which provides the detail of the subscriber database to the other core elements of the home network. The information saved from the user profile can specify to the S-CSCF, whether signaling can be done to the subscriber or not, to forward the data packets.

Benefits of IIP architecture:

E-CSCF, Emergency Call Session Control function server, (not used in existing IMS core) resides with the P-CSCF to handle emergency call request during IIP call session. The request for emergency service is established by the P-CSCF, and the session is recognized by validating the location of the subscriber fetched by S-CSCF by communicating with the HSS, and the call is forwarded to the appropriate destination. Additionally, IIP architecture also includes interworking entities, such a BGCF, MGCF and MGW to route the SIP signaling to the destined subscriber of underlying heterogeneous Media/connectivity layer. The main function of Breakout Gateway Control Function (BGCF), is to offer a connection between IIP packets switched network and Non-IIP (PSTN) circuit switched network. The servers, MGCF, MGW and SGW, collectively work as a gateway through which BGCF transport the signaling messages. Media Gateway control function (MGCF), SIP signaling entity manages the distribution of session across multiple media gateway. Media Gateway (MGW) encodes and decodes the media exchanged between the IIP and circuit switched network. It eradicates the barrier between PSTN circuit switching, IMS and non-IMS network interconnected at the media /connectivity layer. Multimedia Resource Function Controller (MRCF) performs media processing required by the application server. It controls the Media Resource Function Processor (MRFP) to transfer and retrieve the media stream resource such as conferencing, voice mail, recording, voice processing etc., from the application server and S-CSCF.

The IIP provide a horizontal integration of services and access independence supporting functions such as authentication, addressing, routing capability negotiation, service invocation, provisioning, charging, session establishment, etc. • IIP with common unified architecture of all underlying layers can provide a converged access to all multimedia service such as voice, data and video, with both circuit and packet switching network. IIP brings the internet power to the communication world with reduced revenue for both fixed and mobile applications. • The interworking of the connectivity, call control and application servers to a common interface, provide ease of access to all user’s independent of the location and mobility. • The flat architecture of IIP endow various service providers, with different modules, architecture, and protocols to interwork without much modifications to network element, thus reducing cot of implementation of new services. • IIP also provides security and QoS functionality to make a complete platform to serve next generation network. • It enables new services, video sharing; Instant Messaging (IM); VoIP; Push-to-Talk over Cellular (PoC); conferencing; rich-call features; multimedia gaming; and voice messaging, etc., with uniform and flexible billing options. IV.

APPLICATION CENTRIC RESOURCE ALLOCATION SCHEME

B. Service and Application Layer The application server layer is the execution platform for one or more application servers that control the end service based on user requirement [13]. A wide variety of servers can be included to support both real time and non real time services due to the flexibility provided in the IIP architecture, as it forms a unified layered architecture with all layers connected commonly to the cloud. The different servers included in AS to make IIP a reality to support the varied service availability for digital convergence are Telephony Application Server (TAS), IP Multimedia - Service Switching Function (IM-SSF), Service Centralization and Continuity Application Server (SCC-AS), IP-Short Message Gateway (IPSM-GW), Multimedia Telephony (MMTel), Open Service Access Gateway (OSA-GW), etc. Each AS performs its own functionality and communicates with the call control layer elements via SIP signaling, depending upon the service invoked by the end user. IIP unified service layer with Control and Connectivity layer, provide a flexible support to wide choice of end-user services include multiparty gaming,

Figure 2 Dynamic Application Centric Radio Resource Management Scheme

The Dynamic Application Centric Resource Provisioning (DAC- RP) algorithm is a complete partition scheme, in which the available net bandwidth among the interworking network is divided into separate pools for each traffic type. In this scheme, the boundary for the partition is movable and, thus, can effectively deal with the traffic changes in the system. As shown in Fig. 2, C1 out of C channels are reserved exclusively for new Real-time services (RTS) and C2 channels are reserved exclusively for Non Real-time Services (NRTS). The other (C-C1-C2) channels (shared area) are to be shared in a fair manner by both Real-time and Non-real time applications.

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In order to maintain a low handoff dropping probability for RTS, it is further restricted, that new RTS can only use the C3 out of C1 channels. However, Handoff Real-time services (HRTS) can use any channel available in C1 and also in C4. The admission control of DAC-RP Algorithm described as follows. For a new RTS request to a network, unoccupied channels in C3 searched, if there is no available channel there, the free channel in C4 (shared Channel area) will be searched. Likewise, an NRTS call assignment will be first attempted in the C2 area and if that is not possible, the channels available in C4 will be examined. When an H-RTS call arrives, it searches for available channels in C1 and in C4 and, if there is no channel available in both, the call will be dropped. When a new RTS call arrives, and if the channel occupancy exceeds the threshold in C3 and C4 (i.e. there is no idle channel), the call will be blocked. When an NRTS call (new or handoff) arrive, if the number of idle channels in the C2 or in C4 area is lesser than B( Bandwidth for NRTS), it will be blocked. The DAC-RP scheme can be modelled as a threedimensional Markov chain [14]. Let Pijk be the steady probability with, i, j and k representing the new RTS calls, HRTS and NRTS in the Interworking system respectively. The resource selection conditions, for allocating the calls to the appropriate available channel is given in Table.1 TABLE I. CHANNEL CONDITION BASED ON RESOURCE ALLOCATION Channel Condition

RTS 0 99 99 9

Call Acceptance NRTS H- RTS 0 0 99 99 8 99 8 9

i=j=k=0 0 ≤ i+j ≤ C1& 0 ≤ k< [(C - C1) /B] 0< i + j Ns. ⎜ ⎟ 1− e ⎝ μs ⎠

)

(6)

h3 =

Psavings =

and,

(

]

μp

t t −λ pci o ⎞ −λ t −λ pci ( k −1) o ⎛ 1 ⎞⎛ α + Pk = ⎜1 − ⎟ ⎜1 − e α ⎟ e pci i e μ s ⎠⎝ ⎝ ⎠ t to −λ s o ⎞ −λ pci ( k −1) ⎛ 1 ⎞⎛ −λ s ti α α − e e e 1 for 1≤ k ≤ N, ⎟ ⎜ ⎟⎜ μ ⎝ s ⎠⎝ ⎠

Value 0.4 0.05 6 20ms 0.0625

RESULT ANALYSIS

The performance metrics such as power savings and wake up delay of LTE UE using the DRX mechanism with the incorporation of OTSC ratio in 4 state Markov chain model is determined and analyzed. The simulations are carried out using the MATLAB. Table 3 indicates the parameters used for the simulation as mentioned in LTE standards specification [4]. The simulation result shows power savings using DRX mechanisms with the help of corresponding timer values. As the wake up delay estimations provide the trade-off relation with power savings, the impact of each timer parameters of DRX mechanism on power savings and delay are discussed in this section. Moreover, the work illustrated in this paper has been compared with an existing 4 state model [13] proposed by S. Fowler et al. The performance analysis in terms of power saving and wakeup delay of four state DRX model with the incorporation of the OTSC are depicted from Fig. 3 to Fig. 12. The figures indicate the analytical results of the tradeoff parameters such as power savings and delay. On comparing the timers of DRX parameters, the inactivity timer ti consumes the highest power. Hence the performance analysis on power savings and delay are made initially with the inactivity timer values. Fig. 3 and Fig. 4 distinctly illustrate the trade off relation between power savings and delay. Moreover, the performance analysis on power savings for various values of inactivity timers are made with fixed values of DRX parameters such as Ns = 6, Tl = 20ms , λipc = 0.4 and λis = 0.05. The figure illustrates a significant improvement of 10.23% power savings compared to that of existing method [13]. It is also noted that for increasing inactivity timer values, the power saving decreases and saturates at 61.67% from 200 ms and above , as the UE stays for more duration in the active state. Fig. 4 depicts the delay response of the proposed and existing system for increasing inactivity timer values. Approximately an enhancement of 10.23% power savings is achieved at the cost of 0.75 % increased delay.

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Figure 3. Power Savings Vs Inactivity Timer λipc = 0.4, λis = 0.05, Ns = 6, Tl = 20ms

Figure 4. Delay Vs Inactivity Timer, λipc = 0.4, λis = 0.05, Ns = 6, Tl = 20ms

Subsequently, the next power consuming timer has been identified as DRX short cycle Timer Ns. Hence the next analysis is focused on other significant power savings timing parameter DRX Short Cycle Timer Ns. Fig. 5 and Fig. 6 shows the analytical results on power savings and the delay with respect to DRX short cycle timer by considering fixed values of λipc = 0.4, λis = 0.05, Tl = 20ms. From the Fig. 5 it is observed that an improvement of 0.91% of power savings is achieved compared to that of an existing method. However, the figure significantly illustrates the decrement in power savings for the increasing DRX short cycle timer Ns. The frequent occurrences of wakeup timer activation for higher values of DRX short cycle timer made UE to stay tuned for more number of short active periods. These short active periods consumes specific amount of powers which becomes a reason of reduction in power savings for increasing DRX Short Cycle timer Ns. The performance analyses of power savings and delay metrics with respect to DRX long cycle has been made for the fixed values of λipc = 0.4, λis = 0.05, Ns=6. The responses of these metrics are illustrated in the Fig. 7 and Fig. 8. The study has shown that 0.4% of overall improvement in power savings is achieved for the constant α = 0.0625. It is also identified that

Figure 5. Power Savings Vs DRX Short Cycle Timer, λipc = 0.4, λis = 0.05, Tl = 20ms

Figure 6. Delay Vs DRX Short Cycle Timer, λipc = 0.4, λis = 0.05, Tl = 20ms

power savings shows significant improvements for the Tl values ranges above 330ms. The improvement of the power savings are due to the deep sleep mode provided by the DRX long cycle by switching off the RF circuits for longer period than the DRX short cycle timer. The Fig. 8 depicts the proportionate increase in the delay for increasing values of the DRX long cycle timer. Though the delay and power savings are tradeoff to each other, the power savings of 0.4% is achieved for all values with respect to Tl through the proposed technique compared to that of existing technique at cost of an approximate increase in 2.2% of delay. The power savings and the delay analyses for different arrival rate of inter packet are shown in the Fig. 9 and Fig. 10 respectively. Fig. 9 shows the analysis of power savings for different values of λip and at constant values of λis = 0.05, Tl = 20 ms, Ns = 6. It also indicates that an improvement of 2.08% is achieved for the values of λip compared to that of an existing one. The Fig. 10 depicts the impact of the delay conversely for the λip arrival rates. The Fig. 9 and Fig. 10 illustrates that a significant enhancement of power savings of 2.08% is achieved at the cost of 1.87% increased delay for the inter packet arrival rate λip through the proposed technique which does not affect the quality of service.

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Figure 9.

Delay Vs Interpacket arrival rate λip , λis = 0.05, Tl = 20 ms, Ns = 6

Figure 7. Power Saving Factor Vs DRX Long Cycle, λipc = 0.4, λis = 0.05,Ns=6

Figure 10. Power Saving Factor Vs Interpacket arrival rate λip λis = 0.05, Tl = 20 ms, Ns = 6 Figure 8. Delay Vs DRX Long Cycle λipc = 0.4, λis = 0.05, Ns = 6

Analyzing the arrival rate in terms of inter session packets provides the another dimension that determines the power savings in the UE. Fig. 11 and Fig. 12 depicts the performance analyses of power savings and the delays respectively with respect to inter session packet arrivals. Both the metrics are evaluated with the fixed values of λip = 0.04, Tl = 20 ms, Ns = 6 but for various values of λis. The improved power savings of 1.08% has been identified for the lower value of α = 0.0625 using the proposed technique at a cost of 3.26% of increased delay. Though the increased delay is at the higher value, it has been identified that it satisfies the minimum delay range to maintain the required Quality of Service. At the end result of succession, simulation results illustrated from Fig. 3 to Fig. 12 represent the complete power saving and delay analyses to key out the better power savings with the optimized delay by assigning the value for α = 0.0625. As the assignment of α value depends on the factor of On Timer duration, it has been identified that the selection of α ≥ 1 must be avoided. Because, the pessimal response on power saving performance by receding power savings to lesser than 50% is

obtained on the assignment of α values greater than 1. So, it is not suitable for power constrained application of LTE networks. Henceforth, the value of α lesser than 0.1 exhibits the balanced tradeoff between power and delay which can be considered for the real time applications of LTE networks. V.

CONCLUSION

In this paper, an attempt has been made to improve the power savings of the User Equipment of Long term evolution by using 4 state DRX model with the incorporation of On Timer to Short Cycle (OTSC) ratio α. The analytical approach on ETSI traffic pattern with bursty traffic model is made to identify improvements in the power savings. The performance analyses of both the power saving and delay is done by using MATLAB. The impact on proposed model has shown the better performance in terms of power saving compared to that of existing DRX mechanism with a minimum trade off in the delay. Further, power savings and wakeup delay analyses for the different inter packet and

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[6]

[7]

[8]

Figure 11. Power Savings Vs Intersession arrival rate λis , λip = 0.04, Tl = 20 ms, Ns = 6

[9]

[10]

[11]

[12]

[13] Figure 12.

Delay Vs Intersession arrival rate λis , λip = 0.04, Tl = 20 ms, Ns = 6

intersession arrival rates are made. It is verified through the simulation results that good improvement in power savings is achieved with minimum wakeup delay raise that does not affect the Long term Evolution’s Quality of Service. The analyses also provide the details of choosing the DRX parameters for various packet arrival rate to improve the power savings of the User Equipment battery in LTE. The work can be extended further as an interest of future work to key out the better power savings using 5 state Markov model.

26th Convention of Electrical and Electronics Engineers in Israel (IEEEI), Israel, pp. 520–0524,2010. L. Zhou, H. Xu, H. Tian, Y. Gao, L. Du, L. Chen, Performance analysis of power saving mechanism with adjustable drx cycles in 3GPP LTE, Proceedings in: IEEE Vehicular Technology Conference (VTC–Fall), Calgary BC, pp. 1–5, Sept. 2008. E. Liu, J. Zhang, W. Ren, Adaptive DRX scheme for beyond 3G mobile handsets, Proceedings in: IEEE Global Telecommunications Conference (GLOBECOM), Houston, USA, pp. 1–5, Dec. 2011. J.Wigard, T. Kolding, L. Dalsgaard, C. Coletti, On the user performance of lte ue power savings schemes with discontinuous reception in LTE, Proceedings in: IEEE International Conference on Communications Workshops, Dresden, pp.1-5, Jul. 2009. M. Polignano, D. Vinella, D. Laselva, J. Wigard, T. Sorensens, Power savings and QoS impact for VoIP application with DRX/DTX feature in LTE, Proceedings in: IEEE Vehicular Technology Conference (VTC– Spring), pp. 1-5, 2011 S. Jha, A. Ko, R. Vannithamby, Optimization of discontinuous reception (drx) for mobile internet applications over LTE, Proceedings in: IEEE Vehicular Technology Conference (VTC–Fall),Quebec City, pp 1-5, Sept.2012. S. Jin, D. Qiao, Numerical analysis of the power saving in 3GGP LTE advanced wireless networks, IEEE Transactions on Vehicular Technology , Vol.61, no.4,pp. 1779-1785, 2012. K.T.V. Kumar, R. Vassoudevan, P.Samundiswary, Performance evaluation for LTE using 4 state Discontinuous Reception (DRX) mechanism with On Time to Short Cycle (OTSC) ratio, Proceedings in: IEEE International Conference on Control, Instrumentation, Communication and Computational Technologies, Kumarakoil, India, pp. 451-456, Dec. 2015. Scott A.Fowler, Abdelhamid Mellouk, Naomi Yamada, LTE-Advanced DRX Mechanism for Power Saving, Wiley Publishers,ISBN:978-184821-532.

AUTHORS PROFILE R. Vassoudevan received his B.E degree in Electronics and Communication Engineering from Karnataka University, Dharwad, India in 1996 and M.E degree in Applied Electronics from Sathyabama University, Chennai, India in 2007. At present, he is pursuing his Ph.D. in the Department of Electronics Engineering Pondicherry University, Puducherry, India. His current area of research interest is in the field of Wireless Communication.

REFERENCES [1]

[2]

[3]

[4] [5]

3GPP TS 25.304, User Equipment (UE) procedures in idle mode and procedures for cell reselection in connected mode, TS 25.304, 3rd Generation Partnership Project (3GPP) (Sep. 2005). 3GPP TS 36.321, Evolved Universal Terrestrial Radio Access (EUTRA); Medium Access Control (MAC) protocol specification, TS 36.321,3rd Generation Partnership Project (3GPP) Sep. 2008. S.-R. Yang, S.-Y. Yan, H.-N. Hung, Modeling UMTS power saving with bursty packet data traffic, IEEE Transactions on Mobile Computing, Vol.6, no.12, pp.1398–1409, 2007. C. Bontu, E. Illidge, Drx mechanism for power saving in LTE, IEEE Communications Magazine, Vol. 47,no.6,pp. 48-55, 2009. Y. Mihov, K. Kassev, B. Tsankov, Analysis and performance evaluation of the drx mechanism for power saving in LTE, Proceedings in: IEEE

P. Samundiswary received her B.Tech degree and M.Tech degree in Electronics and Communication Engineering from Pondicherry Engineering College affiliated to Pondicherry University, Pondicherry, India in 1997 and 2003 respectively. She received her Ph. D degree from Pondicherry Engineering College affiliated to Pondicherry University, Pondicherry, India in 2011. She has been working in teaching profession since 1998. Presently, she is working as Assistant Professor in the Department of Electronics Engineering, School of Engineering and Technology, Pondicherry Central University, India. She has nearly 18 years of teaching experience. She has published more than 70 papers in national and international conference proceedings and journals. She has co-authored a chapter of the book published by INTECH Publishers. She has been one of the authors of the book published by LAMBERT Academic Publishing. Her area of interest includes Wireless Communication and Networks, Wireless Security and Computer Networks. She will be available at [email protected]

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International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Assist. Prof. Nisheeth Joshi, Apaji Institute, Banasthali University, Rajasthan, India Associate Prof. Kunwar S. Vaisla, VCT Kumaon Engineering College, India Prof Anupam Choudhary, Bhilai School Of Engg.,Bhilai (C.G.),India Mr. Divya Prakash Shrivastava, Al Jabal Al garbi University, Zawya, Libya Associate Prof. Dr. V. Radha, Avinashilingam Deemed university for women, Coimbatore. Dr. Kasarapu Ramani, JNT University, Anantapur, India Dr. Anuraag Awasthi, Jayoti Vidyapeeth Womens University, India Dr. C G Ravichandran, R V S College of Engineering and Technology, India Dr. Mohamed A. Deriche, King Fahd University of Petroleum and Minerals, Saudi Arabia Mr. Abbas Karimi, Universiti Putra Malaysia, Malaysia Mr. Amit Kumar, Jaypee University of Engg. and Tech., India Dr. Nikolai Stoianov, Defense Institute, Bulgaria Assist. Prof. S. Ranichandra, KSR College of Arts and Science, Tiruchencode Mr. T.K.P. Rajagopal, Diamond Horse International Pvt Ltd, India Dr. Md. Ekramul Hamid, Rajshahi University, Bangladesh Mr. Hemanta Kumar Kalita , TATA Consultancy Services (TCS), India Dr. Messaouda Azzouzi, Ziane Achour University of Djelfa, Algeria Prof. (Dr.) Juan Jose Martinez Castillo, "Gran Mariscal de Ayacucho" University and Acantelys research Group, Venezuela Dr. Jatinderkumar R. Saini, Narmada College of Computer Application, India Dr. Babak Bashari Rad, University Technology of Malaysia, Malaysia Dr. Nighat Mir, Effat University, Saudi Arabia Prof. (Dr.) G.M.Nasira, Sasurie College of Engineering, India Mr. Varun Mittal, Gemalto Pte Ltd, Singapore Assist. Prof. Mrs P. Banumathi, Kathir College Of Engineering, Coimbatore Assist. Prof. Quan Yuan, University of Wisconsin-Stevens Point, US Dr. Pranam Paul, Narula Institute of Technology, Agarpara, West Bengal, India Assist. Prof. J. Ramkumar, V.L.B Janakiammal college of Arts & Science, India Mr. P. Sivakumar, Anna university, Chennai, India Mr. Md. Humayun Kabir Biswas, King Khalid University, Kingdom of Saudi Arabia Mr. Mayank Singh, J.P. Institute of Engg & Technology, Meerut, India HJ. Kamaruzaman Jusoff, Universiti Putra Malaysia Mr. Nikhil Patrick Lobo, CADES, India Dr. Amit Wason, Rayat-Bahra Institute of Engineering & Boi-Technology, India Dr. Rajesh Shrivastava, Govt. Benazir Science & Commerce College, Bhopal, India Assist. Prof. Vishal Bharti, DCE, Gurgaon Mrs. Sunita Bansal, Birla Institute of Technology & Science, India Dr. R. Sudhakar, Dr.Mahalingam college of Engineering and Technology, India Dr. Amit Kumar Garg, Shri Mata Vaishno Devi University, Katra(J&K), India Assist. Prof. Raj Gaurang Tiwari, AZAD Institute of Engineering and Technology, India Mr. Hamed Taherdoost, Tehran, Iran Mr. Amin Daneshmand Malayeri, YRC, IAU, Malayer Branch, Iran Mr. Shantanu Pal, University of Calcutta, India Dr. Terry H. Walcott, E-Promag Consultancy Group, United Kingdom Dr. Ezekiel U OKIKE, University of Ibadan, Nigeria Mr. P. Mahalingam, Caledonian College of Engineering, Oman Dr. Mahmoud M. A. Abd Ellatif, Mansoura University, Egypt

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Prof. Kunwar S. Vaisla, BCT Kumaon Engineering College, India Prof. Mahesh H. Panchal, Kalol Institute of Technology & Research Centre, India Mr. Muhammad Asad, Technical University of Munich, Germany Mr. AliReza Shams Shafigh, Azad Islamic university, Iran Prof. S. V. Nagaraj, RMK Engineering College, India Mr. Ashikali M Hasan, Senior Researcher, CelNet security, India Dr. Adnan Shahid Khan, University Technology Malaysia, Malaysia Mr. Prakash Gajanan Burade, Nagpur University/ITM college of engg, Nagpur, India Dr. Jagdish B.Helonde, Nagpur University/ITM college of engg, Nagpur, India Professor, Doctor BOUHORMA Mohammed, Univertsity Abdelmalek Essaadi, Morocco Mr. K. Thirumalaivasan, Pondicherry Engg. College, India Mr. Umbarkar Anantkumar Janardan, Walchand College of Engineering, India Mr. Ashish Chaurasia, Gyan Ganga Institute of Technology & Sciences, India Mr. Sunil Taneja, Kurukshetra University, India Mr. Fauzi Adi Rafrastara, Dian Nuswantoro University, Indonesia Dr. Yaduvir Singh, Thapar University, India Dr. Ioannis V. Koskosas, University of Western Macedonia, Greece Dr. Vasantha Kalyani David, Avinashilingam University for women, Coimbatore Dr. Ahmed Mansour Manasrah, Universiti Sains Malaysia, Malaysia Miss. Nazanin Sadat Kazazi, University Technology Malaysia, Malaysia Mr. Saeed Rasouli Heikalabad, Islamic Azad University - Tabriz Branch, Iran Assoc. Prof. Dhirendra Mishra, SVKM's NMIMS University, India Prof. Shapoor Zarei, UAE Inventors Association, UAE Prof. B.Raja Sarath Kumar, Lenora College of Engineering, India Dr. Bashir Alam, Jamia millia Islamia, Delhi, India Prof. Anant J Umbarkar, Walchand College of Engg., India Assist. Prof. B. Bharathi, Sathyabama University, India Dr. Fokrul Alom Mazarbhuiya, King Khalid University, Saudi Arabia Prof. T.S.Jeyali Laseeth, Anna University of Technology, Tirunelveli, India Dr. M. Balraju, Jawahar Lal Nehru Technological University Hyderabad, India Dr. Vijayalakshmi M. N., R.V.College of Engineering, Bangalore Prof. Walid Moudani, Lebanese University, Lebanon Dr. Saurabh Pal, VBS Purvanchal University, Jaunpur, India Associate Prof. Suneet Chaudhary, Dehradun Institute of Technology, India Associate Prof. Dr. Manuj Darbari, BBD University, India Ms. Prema Selvaraj, K.S.R College of Arts and Science, India Assist. Prof. Ms.S.Sasikala, KSR College of Arts & Science, India Mr. Sukhvinder Singh Deora, NC Institute of Computer Sciences, India Dr. Abhay Bansal, Amity School of Engineering & Technology, India Ms. Sumita Mishra, Amity School of Engineering and Technology, India Professor S. Viswanadha Raju, JNT University Hyderabad, India Mr. Asghar Shahrzad Khashandarag, Islamic Azad University Tabriz Branch, India Mr. Manoj Sharma, Panipat Institute of Engg. & Technology, India Mr. Shakeel Ahmed, King Faisal University, Saudi Arabia Dr. Mohamed Ali Mahjoub, Institute of Engineer of Monastir, Tunisia Mr. Adri Jovin J.J., SriGuru Institute of Technology, India Dr. Sukumar Senthilkumar, Universiti Sains Malaysia, Malaysia

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Mr. Rakesh Bharati, Dehradun Institute of Technology Dehradun, India Mr. Shervan Fekri Ershad, Shiraz International University, Iran Mr. Md. Safiqul Islam, Daffodil International University, Bangladesh Mr. Mahmudul Hasan, Daffodil International University, Bangladesh Prof. Mandakini Tayade, UIT, RGTU, Bhopal, India Ms. Sarla More, UIT, RGTU, Bhopal, India Mr. Tushar Hrishikesh Jaware, R.C. Patel Institute of Technology, Shirpur, India Ms. C. Divya, Dr G R Damodaran College of Science, Coimbatore, India Mr. Fahimuddin Shaik, Annamacharya Institute of Technology & Sciences, India Dr. M. N. Giri Prasad, JNTUCE,Pulivendula, A.P., India Assist. Prof. Chintan M Bhatt, Charotar University of Science And Technology, India Prof. Sahista Machchhar, Marwadi Education Foundation's Group of institutions, India Assist. Prof. Navnish Goel, S. D. College Of Enginnering & Technology, India Mr. Khaja Kamaluddin, Sirt University, Sirt, Libya Mr. Mohammad Zaidul Karim, Daffodil International, Bangladesh Mr. M. Vijayakumar, KSR College of Engineering, Tiruchengode, India Mr. S. A. Ahsan Rajon, Khulna University, Bangladesh Dr. Muhammad Mohsin Nazir, LCW University Lahore, Pakistan Mr. Mohammad Asadul Hoque, University of Alabama, USA Mr. P.V.Sarathchand, Indur Institute of Engineering and Technology, India Mr. Durgesh Samadhiya, Chung Hua University, Taiwan Dr Venu Kuthadi, University of Johannesburg, Johannesburg, RSA Dr. (Er) Jasvir Singh, Guru Nanak Dev University, Amritsar, Punjab, India Mr. Jasmin Cosic, Min. of the Interior of Una-sana canton, B&H, Bosnia and Herzegovina Dr S. Rajalakshmi, Botho College, South Africa Dr. Mohamed Sarrab, De Montfort University, UK Mr. Basappa B. Kodada, Canara Engineering College, India Assist. Prof. K. Ramana, Annamacharya Institute of Technology and Sciences, India Dr. Ashu Gupta, Apeejay Institute of Management, Jalandhar, India Assist. Prof. Shaik Rasool, Shadan College of Engineering & Technology, India Assist. Prof. K. Suresh, Annamacharya Institute of Tech & Sci. Rajampet, AP, India Dr . G. Singaravel, K.S.R. College of Engineering, India Dr B. G. Geetha, K.S.R. College of Engineering, India Assist. Prof. Kavita Choudhary, ITM University, Gurgaon Dr. Mehrdad Jalali, Azad University, Mashhad, Iran Megha Goel, Shamli Institute of Engineering and Technology, Shamli, India Mr. Chi-Hua Chen, Institute of Information Management, National Chiao-Tung University, Taiwan (R.O.C.) Assoc. Prof. A. Rajendran, RVS College of Engineering and Technology, India Assist. Prof. S. Jaganathan, RVS College of Engineering and Technology, India Assoc. Prof. (Dr.) A S N Chakravarthy, JNTUK University College of Engineering Vizianagaram (State University) Assist. Prof. Deepshikha Patel, Technocrat Institute of Technology, India Assist. Prof. Maram Balajee, GMRIT, India Assist. Prof. Monika Bhatnagar, TIT, India Prof. Gaurang Panchal, Charotar University of Science & Technology, India Prof. Anand K. Tripathi, Computer Society of India Prof. Jyoti Chaudhary, High Performance Computing Research Lab, India Assist. Prof. Supriya Raheja, ITM University, India

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Dr. Pankaj Gupta, Microsoft Corporation, U.S.A. Assist. Prof. Panchamukesh Chandaka, Hyderabad Institute of Tech. & Management, India Prof. Mohan H.S, SJB Institute Of Technology, India Mr. Hossein Malekinezhad, Islamic Azad University, Iran Mr. Zatin Gupta, Universti Malaysia, Malaysia Assist. Prof. Amit Chauhan, Phonics Group of Institutions, India Assist. Prof. Ajal A. J., METS School Of Engineering, India Mrs. Omowunmi Omobola Adeyemo, University of Ibadan, Nigeria Dr. Bharat Bhushan Agarwal, I.F.T.M. University, India Md. Nazrul Islam, University of Western Ontario, Canada Tushar Kanti, L.N.C.T, Bhopal, India Er. Aumreesh Kumar Saxena, SIRTs College Bhopal, India Mr. Mohammad Monirul Islam, Daffodil International University, Bangladesh Dr. Kashif Nisar, University Utara Malaysia, Malaysia Dr. Wei Zheng, Rutgers Univ/ A10 Networks, USA Associate Prof. Rituraj Jain, Vyas Institute of Engg & Tech, Jodhpur – Rajasthan Assist. Prof. Apoorvi Sood, I.T.M. University, India Dr. Kayhan Zrar Ghafoor, University Technology Malaysia, Malaysia Mr. Swapnil Soner, Truba Institute College of Engineering & Technology, Indore, India Ms. Yogita Gigras, I.T.M. University, India Associate Prof. Neelima Sadineni, Pydha Engineering College, India Pydha Engineering College Assist. Prof. K. Deepika Rani, HITAM, Hyderabad Ms. Shikha Maheshwari, Jaipur Engineering College & Research Centre, India Prof. Dr V S Giridhar Akula, Avanthi's Scientific Tech. & Research Academy, Hyderabad Prof. Dr.S.Saravanan, Muthayammal Engineering College, India Mr. Mehdi Golsorkhatabar Amiri, Islamic Azad University, Iran Prof. Amit Sadanand Savyanavar, MITCOE, Pune, India Assist. Prof. P.Oliver Jayaprakash, Anna University,Chennai Assist. Prof. Ms. Sujata, ITM University, Gurgaon, India Dr. Asoke Nath, St. Xavier's College, India Mr. Masoud Rafighi, Islamic Azad University, Iran Assist. Prof. RamBabu Pemula, NIMRA College of Engineering & Technology, India Assist. Prof. Ms Rita Chhikara, ITM University, Gurgaon, India Mr. Sandeep Maan, Government Post Graduate College, India Prof. Dr. S. Muralidharan, Mepco Schlenk Engineering College, India Associate Prof. T.V.Sai Krishna, QIS College of Engineering and Technology, India Mr. R. Balu, Bharathiar University, Coimbatore, India Assist. Prof. Shekhar. R, Dr.SM College of Engineering, India Prof. P. Senthilkumar, Vivekanandha Institue of Engineering and Techology for Woman, India Mr. M. Kamarajan, PSNA College of Engineering & Technology, India Dr. Angajala Srinivasa Rao, Jawaharlal Nehru Technical University, India Assist. Prof. C. Venkatesh, A.I.T.S, Rajampet, India Mr. Afshin Rezakhani Roozbahani, Ayatollah Boroujerdi University, Iran Mr. Laxmi chand, SCTL, Noida, India Dr. Dr. Abdul Hannan, Vivekanand College, Aurangabad Prof. Mahesh Panchal, KITRC, Gujarat Dr. A. Subramani, K.S.R. College of Engineering, Tiruchengode

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Assist. Prof. Prakash M, Rajalakshmi Engineering College, Chennai, India Assist. Prof. Akhilesh K Sharma, Sir Padampat Singhania University, India Ms. Varsha Sahni, Guru Nanak Dev Engineering College, Ludhiana, India Associate Prof. Trilochan Rout, NM Institute of Engineering and Technlogy, India Mr. Srikanta Kumar Mohapatra, NMIET, Orissa, India Mr. Waqas Haider Bangyal, Iqra University Islamabad, Pakistan Dr. S. Vijayaragavan, Christ College of Engineering and Technology, Pondicherry, India Prof. Elboukhari Mohamed, University Mohammed First, Oujda, Morocco Dr. Muhammad Asif Khan, King Faisal University, Saudi Arabia Dr. Nagy Ramadan Darwish Omran, Cairo University, Egypt. Assistant Prof. Anand Nayyar, KCL Institute of Management and Technology, India Mr. G. Premsankar, Ericcson, India Assist. Prof. T. Hemalatha, VELS University, India Prof. Tejaswini Apte, University of Pune, India Dr. Edmund Ng Giap Weng, Universiti Malaysia Sarawak, Malaysia Mr. Mahdi Nouri, Iran University of Science and Technology, Iran Associate Prof. S. Asif Hussain, Annamacharya Institute of technology & Sciences, India Mrs. Kavita Pabreja, Maharaja Surajmal Institute (an affiliate of GGSIP University), India Mr. Vorugunti Chandra Sekhar, DA-IICT, India Mr. Muhammad Najmi Ahmad Zabidi, Universiti Teknologi Malaysia, Malaysia Dr. Aderemi A. Atayero, Covenant University, Nigeria Assist. Prof. Osama Sohaib, Balochistan University of Information Technology, Pakistan Assist. Prof. K. Suresh, Annamacharya Institute of Technology and Sciences, India Mr. Hassen Mohammed Abduallah Alsafi, International Islamic University Malaysia (IIUM) Malaysia Mr. Robail Yasrab, Virtual University of Pakistan, Pakistan Mr. R. Balu, Bharathiar University, Coimbatore, India Prof. Anand Nayyar, KCL Institute of Management and Technology, Jalandhar Assoc. Prof. Vivek S Deshpande, MIT College of Engineering, India Prof. K. Saravanan, Anna university Coimbatore, India Dr. Ravendra Singh, MJP Rohilkhand University, Bareilly, India Mr. V. Mathivanan, IBRA College of Technology, Sultanate of OMAN Assoc. Prof. S. Asif Hussain, AITS, India Assist. Prof. C. Venkatesh, AITS, India Mr. Sami Ulhaq, SZABIST Islamabad, Pakistan Dr. B. Justus Rabi, Institute of Science & Technology, India Mr. Anuj Kumar Yadav, Dehradun Institute of technology, India Mr. Alejandro Mosquera, University of Alicante, Spain Assist. Prof. Arjun Singh, Sir Padampat Singhania University (SPSU), Udaipur, India Dr. Smriti Agrawal, JB Institute of Engineering and Technology, Hyderabad Assist. Prof. Swathi Sambangi, Visakha Institute of Engineering and Technology, India Ms. Prabhjot Kaur, Guru Gobind Singh Indraprastha University, India Mrs. Samaher AL-Hothali, Yanbu University College, Saudi Arabia Prof. Rajneeshkaur Bedi, MIT College of Engineering, Pune, India Mr. Hassen Mohammed Abduallah Alsafi, International Islamic University Malaysia (IIUM) Dr. Wei Zhang, Amazon.com, Seattle, WA, USA Mr. B. Santhosh Kumar, C S I College of Engineering, Tamil Nadu Dr. K. Reji Kumar, , N S S College, Pandalam, India

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Assoc. Prof. K. Seshadri Sastry, EIILM University, India Mr. Kai Pan, UNC Charlotte, USA Mr. Ruikar Sachin, SGGSIET, India Prof. (Dr.) Vinodani Katiyar, Sri Ramswaroop Memorial University, India Assoc. Prof., M. Giri, Sreenivasa Institute of Technology and Management Studies, India Assoc. Prof. Labib Francis Gergis, Misr Academy for Engineering and Technology (MET), Egypt Assist. Prof. Amanpreet Kaur, ITM University, India Assist. Prof. Anand Singh Rajawat, Shri Vaishnav Institute of Technology & Science, Indore Mrs. Hadeel Saleh Haj Aliwi, Universiti Sains Malaysia (USM), Malaysia Dr. Abhay Bansal, Amity University, India Dr. Mohammad A. Mezher, Fahad Bin Sultan University, KSA Assist. Prof. Nidhi Arora, M.C.A. Institute, India Prof. Dr. P. Suresh, Karpagam College of Engineering, Coimbatore, India Dr. Kannan Balasubramanian, Mepco Schlenk Engineering College, India Dr. S. Sankara Gomathi, Panimalar Engineering college, India Prof. Anil kumar Suthar, Gujarat Technological University, L.C. Institute of Technology, India Assist. Prof. R. Hubert Rajan, NOORUL ISLAM UNIVERSITY, India Assist. Prof. Dr. Jyoti Mahajan, College of Engineering & Technology Assist. Prof. Homam Reda El-Taj, College of Network Engineering, Saudi Arabia & Malaysia Mr. Bijan Paul, Shahjalal University of Science & Technology, Bangladesh Assoc. Prof. Dr. Ch V Phani Krishna, KL University, India Dr. Vishal Bhatnagar, Ambedkar Institute of Advanced Communication Technologies & Research, India Dr. Lamri LAOUAMER, Al Qassim University, Dept. Info. Systems & European University of Brittany, Dept. Computer Science, UBO, Brest, France Prof. Ashish Babanrao Sasankar, G.H.Raisoni Institute Of Information Technology, India Prof. Pawan Kumar Goel, Shamli Institute of Engineering and Technology, India Mr. Ram Kumar Singh, S.V Subharti University, India Assistant Prof. Sunish Kumar O S, Amaljyothi College of Engineering, India Dr Sanjay Bhargava, Banasthali University, India Mr. Pankaj S. Kulkarni, AVEW's Shatabdi Institute of Technology, India Mr. Roohollah Etemadi, Islamic Azad University, Iran Mr. Oloruntoyin Sefiu Taiwo, Emmanuel Alayande College Of Education, Nigeria Mr. Sumit Goyal, National Dairy Research Institute, India Mr Jaswinder Singh Dilawari, Geeta Engineering College, India Prof. Raghuraj Singh, Harcourt Butler Technological Institute, Kanpur Dr. S.K. Mahendran, Anna University, Chennai, India Dr. Amit Wason, Hindustan Institute of Technology & Management, Punjab Dr. Ashu Gupta, Apeejay Institute of Management, India Assist. Prof. D. Asir Antony Gnana Singh, M.I.E.T Engineering College, India Mrs Mina Farmanbar, Eastern Mediterranean University, Famagusta, North Cyprus Mr. Maram Balajee, GMR Institute of Technology, India Mr. Moiz S. Ansari, Isra University, Hyderabad, Pakistan Mr. Adebayo, Olawale Surajudeen, Federal University of Technology Minna, Nigeria Mr. Jasvir Singh, University College Of Engg., India Mr. Vivek Tiwari, MANIT, Bhopal, India Assoc. Prof. R. Navaneethakrishnan, Bharathiyar College of Engineering and Technology, India Mr. Somdip Dey, St. Xavier's College, Kolkata, India

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Mr. Souleymane Balla-Arabé, Xi’an University of Electronic Science and Technology, China Mr. Mahabub Alam, Rajshahi University of Engineering and Technology, Bangladesh Mr. Sathyapraksh P., S.K.P Engineering College, India Dr. N. Karthikeyan, SNS College of Engineering, Anna University, India Dr. Binod Kumar, JSPM's, Jayawant Technical Campus, Pune, India Assoc. Prof. Dinesh Goyal, Suresh Gyan Vihar University, India Mr. Md. Abdul Ahad, K L University, India Mr. Vikas Bajpai, The LNM IIT, India Dr. Manish Kumar Anand, Salesforce (R & D Analytics), San Francisco, USA Assist. Prof. Dheeraj Murari, Kumaon Engineering College, India Assoc. Prof. Dr. A. Muthukumaravel, VELS University, Chennai Mr. A. Siles Balasingh, St.Joseph University in Tanzania, Tanzania Mr. Ravindra Daga Badgujar, R C Patel Institute of Technology, India Dr. Preeti Khanna, SVKM’s NMIMS, School of Business Management, India Mr. Kumar Dayanand, Cambridge Institute of Technology, India Dr. Syed Asif Ali, SMI University Karachi, Pakistan Prof. Pallvi Pandit, Himachal Pradeh University, India Mr. Ricardo Verschueren, University of Gloucestershire, UK Assist. Prof. Mamta Juneja, University Institute of Engineering and Technology, Panjab University, India Assoc. Prof. P. Surendra Varma, NRI Institute of Technology, JNTU Kakinada, India Assist. Prof. Gaurav Shrivastava, RGPV / SVITS Indore, India Dr. S. Sumathi, Anna University, India Assist. Prof. Ankita M. Kapadia, Charotar University of Science and Technology, India Mr. Deepak Kumar, Indian Institute of Technology (BHU), India Dr. Dr. Rajan Gupta, GGSIP University, New Delhi, India Assist. Prof M. Anand Kumar, Karpagam University, Coimbatore, India Mr. Mr Arshad Mansoor, Pakistan Aeronautical Complex Mr. Kapil Kumar Gupta, Ansal Institute of Technology and Management, India Dr. Neeraj Tomer, SINE International Institute of Technology, Jaipur, India Assist. Prof. Trunal J. Patel, C.G.Patel Institute of Technology, Uka Tarsadia University, Bardoli, Surat Mr. Sivakumar, Codework solutions, India Mr. Mohammad Sadegh Mirzaei, PGNR Company, Iran Dr. Gerard G. Dumancas, Oklahoma Medical Research Foundation, USA Mr. Varadala Sridhar, Varadhaman College Engineering College, Affiliated To JNTU, Hyderabad Assist. Prof. Manoj Dhawan, SVITS, Indore Assoc. Prof. Chitreshh Banerjee, Suresh Gyan Vihar University, Jaipur, India Dr. S. Santhi, SCSVMV University, India Mr. Davood Mohammadi Souran, Ministry of Energy of Iran, Iran Mr. Shamim Ahmed, Bangladesh University of Business and Technology, Bangladesh Mr. Sandeep Reddivari, Mississippi State University, USA Assoc. Prof. Ousmane Thiare, Gaston Berger University, Senegal Dr. Hazra Imran, Athabasca University, Canada Dr. Setu Kumar Chaturvedi, Technocrats Institute of Technology, Bhopal, India Mr. Mohd Dilshad Ansari, Jaypee University of Information Technology, India Ms. Jaspreet Kaur, Distance Education LPU, India Dr. D. Nagarajan, Salalah College of Technology, Sultanate of Oman Dr. K.V.N.R.Sai Krishna, S.V.R.M. College, India

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Mr. Himanshu Pareek, Center for Development of Advanced Computing (CDAC), India Mr. Khaldi Amine, Badji Mokhtar University, Algeria Mr. Mohammad Sadegh Mirzaei, Scientific Applied University, Iran Assist. Prof. Khyati Chaudhary, Ram-eesh Institute of Engg. & Technology, India Mr. Sanjay Agal, Pacific College of Engineering Udaipur, India Mr. Abdul Mateen Ansari, King Khalid University, Saudi Arabia Dr. H.S. Behera, Veer Surendra Sai University of Technology (VSSUT), India Dr. Shrikant Tiwari, Shri Shankaracharya Group of Institutions (SSGI), India Prof. Ganesh B. Regulwar, Shri Shankarprasad Agnihotri College of Engg, India Prof. Pinnamaneni Bhanu Prasad, Matrix vision GmbH, Germany Dr. Shrikant Tiwari, Shri Shankaracharya Technical Campus (SSTC), India Dr. Siddesh G.K., : Dayananada Sagar College of Engineering, Bangalore, India Dr. Nadir Bouchama, CERIST Research Center, Algeria Dr. R. Sathishkumar, Sri Venkateswara College of Engineering, India Assistant Prof (Dr.) Mohamed Moussaoui, Abdelmalek Essaadi University, Morocco Dr. S. Malathi, Panimalar Engineering College, Chennai, India Dr. V. Subedha, Panimalar Institute of Technology, Chennai, India Dr. Prashant Panse, Swami Vivekanand College of Engineering, Indore, India Dr. Hamza Aldabbas, Al-Balqa’a Applied University, Jordan Dr. G. Rasitha Banu, Vel's University, Chennai Dr. V. D. Ambeth Kumar, Panimalar Engineering College, Chennai Prof. Anuranjan Misra, Bhagwant Institute of Technology, Ghaziabad, India Ms. U. Sinthuja, PSG college of arts &science, India Dr. Ehsan Saradar Torshizi, Urmia University, Iran Dr. Shamneesh Sharma, APG Shimla University, Shimla (H.P.), India Assistant Prof. A. S. Syed Navaz, Muthayammal College of Arts & Science, India Assistant Prof. Ranjit Panigrahi, Sikkim Manipal Institute of Technology, Majitar, Sikkim Dr. Khaled Eskaf, Arab Academy for Science ,Technology & Maritime Transportation, Egypt Dr. Nishant Gupta, University of Jammu, India Assistant Prof. Nagarajan Sankaran, Annamalai University, Chidambaram, Tamilnadu, India Assistant Prof.Tribikram Pradhan, Manipal Institute of Technology, India Dr. Nasser Lotfi, Eastern Mediterranean University, Northern Cyprus Dr. R. Manavalan, K S Rangasamy college of Arts and Science, Tamilnadu, India Assistant Prof. P. Krishna Sankar, K S Rangasamy college of Arts and Science, Tamilnadu, India Dr. Rahul Malik, Cisco Systems, USA Dr. S. C. Lingareddy, ALPHA College of Engineering, India Assistant Prof. Mohammed Shuaib, Interal University, Lucknow, India Dr. Sachin Yele, Sanghvi Institute of Management & Science, India Dr. T. Thambidurai, Sun Univercell, Singapore Prof. Anandkumar Telang, BKIT, India Assistant Prof. R. Poorvadevi, SCSVMV University, India Dr Uttam Mande, Gitam University, India Dr. Poornima Girish Naik, Shahu Institute of Business Education and Research (SIBER), India Prof. Md. Abu Kausar, Jaipur National University, Jaipur, India Dr. Mohammed Zuber, AISECT University, India Prof. Kalum Priyanath Udagepola, King Abdulaziz University, Saudi Arabia Dr. K. R. Ananth, Velalar College of Engineering and Technology, India

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Assistant Prof. Sanjay Sharma, Roorkee Engineering & Management Institute Shamli (U.P), India Assistant Prof. Panem Charan Arur, Priyadarshini Institute of Technology, India Dr. Ashwak Mahmood muhsen alabaichi, Karbala University / College of Science, Iraq Dr. Urmila Shrawankar, G H Raisoni College of Engineering, Nagpur (MS), India Dr. Krishan Kumar Paliwal, Panipat Institute of Engineering & Technology, India Dr. Mukesh Negi, Tech Mahindra, India Dr. Anuj Kumar Singh, Amity University Gurgaon, India Dr. Babar Shah, Gyeongsang National University, South Korea Assistant Prof. Jayprakash Upadhyay, SRI-TECH Jabalpur, India Assistant Prof. Varadala Sridhar, Vidya Jyothi Institute of Technology, India Assistant Prof. Parameshachari B D, KSIT, Bangalore, India Assistant Prof. Ankit Garg, Amity University, Haryana, India Assistant Prof. Rajashe Karappa, SDMCET, Karnataka, India Assistant Prof. Varun Jasuja, GNIT, India Assistant Prof. Sonal Honale, Abha Gaikwad Patil College of Engineering Nagpur, India Dr. Pooja Choudhary, CT Group of Institutions, NIT Jalandhar, India Dr. Faouzi Hidoussi, UHL Batna, Algeria Dr. Naseer Ali Husieen, Wasit University, Iraq Assistant Prof. Vinod Kumar Shukla, Amity University, Dubai Dr. Ahmed Farouk Metwaly, K L University Mr. Mohammed Noaman Murad, Cihan University, Iraq Dr. Suxing Liu, Arkansas State University, USA Dr. M. Gomathi, Velalar College of Engineering and Technology, India Assistant Prof. Sumardiono, College PGRI Blitar, Indonesia Dr. Latika Kharb, Jagan Institute of Management Studies (JIMS), Delhi, India Associate Prof. S. Raja, Pauls College of Engineering and Technology, Tamilnadu, India Assistant Prof. Seyed Reza Pakize, Shahid Sani High School, Iran Dr. Thiyagu Nagaraj, University-INOU, India Assistant Prof. Noreen Sarai, Harare Institute of Technology, Zimbabwe Assistant Prof. Gajanand Sharma, Suresh Gyan Vihar University Jaipur, Rajasthan, India Assistant Prof. Mapari Vikas Prakash, Siddhant COE, Sudumbare, Pune, India Dr. Devesh Katiyar, Shri Ramswaroop Memorial University, India Dr. Shenshen Liang, University of California, Santa Cruz, US Assistant Prof. Mohammad Abu Omar, Limkokwing University of Creative Technology- Malaysia Mr. Snehasis Banerjee, Tata Consultancy Services, India Assistant Prof. Kibona Lusekelo, Ruaha Catholic University (RUCU), Tanzania Assistant Prof. Adib Kabir Chowdhury, University College Technology Sarawak, Malaysia Dr. Ying Yang, Computer Science Department, Yale University, USA Dr. Vinay Shukla, Institute Of Technology & Management, India Dr. Liviu Octavian Mafteiu-Scai, West University of Timisoara, Romania Assistant Prof. Rana Khudhair Abbas Ahmed, Al-Rafidain University College, Iraq Assistant Prof. Nitin A. Naik, S.R.T.M. University, India Dr. Timothy Powers, University of Hertfordshire, UK Dr. S. Prasath, Bharathiar University, Erode, India Dr. Ritu Shrivastava, SIRTS Bhopal, India Prof. Rohit Shrivastava, Mittal Institute of Technology, Bhopal, India Dr. Gianina Mihai, Dunarea de Jos" University of Galati, Romania

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Assistant Prof. Ms. T. Kalai Selvi, Erode Sengunthar Engineering College, India Assistant Prof. Ms. C. Kavitha, Erode Sengunthar Engineering College, India Assistant Prof. K. Sinivasamoorthi, Erode Sengunthar Engineering College, India Assistant Prof. Mallikarjun C Sarsamba Bheemnna Khandre Institute Technology, Bhalki, India Assistant Prof. Vishwanath Chikaraddi, Veermata Jijabai technological Institute (Central Technological Institute), India Assistant Prof. Dr. Ikvinderpal Singh, Trai Shatabdi GGS Khalsa College, India Assistant Prof. Mohammed Noaman Murad, Cihan University, Iraq Professor Yousef Farhaoui, Moulay Ismail University, Errachidia, Morocco Dr. Parul Verma, Amity University, India Professor Yousef Farhaoui, Moulay Ismail University, Errachidia, Morocco Assistant Prof. Madhavi Dhingra, Amity University, Madhya Pradesh, India Assistant Prof.. G. Selvavinayagam, SNS College of Technology, Coimbatore, India Assistant Prof. Madhavi Dhingra, Amity University, MP, India Professor Kartheesan Log, Anna University, Chennai Professor Vasudeva Acharya, Shri Madhwa vadiraja Institute of Technology, India Dr. Asif Iqbal Hajamydeen, Management & Science University, Malaysia Assistant Prof., Mahendra Singh Meena, Amity University Haryana Assistant Professor Manjeet Kaur, Amity University Haryana Dr. Mohamed Abd El-Basset Matwalli, Zagazig University, Egypt Dr. Ramani Kannan, Universiti Teknologi PETRONAS, Malaysia Assistant Prof. S. Jagadeesan Subramaniam, Anna University, India Assistant Prof. Dharmendra Choudhary, Tripura University, India Assistant Prof. Deepika Vodnala, SR Engineering College, India Dr. Kai Cong, Intel Corporation & Computer Science Department, Portland State University, USA Dr. Kailas R Patil, Vishwakarma Institute of Information Technology (VIIT), India Dr. Omar A. Alzubi, Faculty of IT / Al-Balqa Applied University, Jordan Assistant Prof. Kareemullah Shaik, Nimra Institute of Science and Technology, India Assistant Prof. Chirag Modi, NIT Goa Dr. R. Ramkumar, Nandha Arts And Science College, India Dr. Priyadharshini Vydhialingam, Harathiar University, India Dr. P. S. Jagadeesh Kumar, DBIT, Bangalore, Karnataka Dr. Vikas Thada, AMITY University, Pachgaon Dr. T. A. Ashok Kumar, Institute of Management, Christ University, Bangalore Dr. Shaheera Rashwan, Informatics Research Institute Dr. S. Preetha Gunasekar, Bharathiyar University, India Asst Professor Sameer Dev Sharma, Uttaranchal University, Dehradun Dr. Zhihan lv, Chinese Academy of Science, China Dr. Ikvinderpal Singh, Trai Shatabdi GGS Khalsa College, Amritsar Dr. Umar Ruhi, University of Ottawa, Canada Dr. Jasmin Cosic, University of Bihac, Bosnia and Herzegovina Dr. Homam Reda El-Taj, University of Tabuk, Kingdom of Saudi Arabia Dr. Mostafa Ghobaei Arani, Islamic Azad University, Iran Dr. Ayyasamy Ayyanar, Annamalai University, India Dr. Selvakumar Manickam, Universiti Sains Malaysia, Malaysia Dr. Murali Krishna Namana, GITAM University, India Dr. Smriti Agrawal, Chaitanya Bharathi Institute of Technology, Hyderabad, India Professor Vimalathithan Rathinasabapathy, Karpagam College Of Engineering, India

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Dr. Sushil Chandra Dimri, Graphic Era University, India Dr. Dinh-Sinh Mai, Le Quy Don Technical University, Vietnam Dr. S. Rama Sree, Aditya Engg. College, India Dr. Ehab T. Alnfrawy, Sadat Academy, Egypt Dr. Patrick D. Cerna, Haramaya University, Ethiopia Dr. Vishal Jain, Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), India Associate Prof. Dr. Jiliang Zhang, North Eastern University, China Dr. Sharefa Murad, Middle East University, Jordan Dr. Ajeet Singh Poonia, Govt. College of Engineering & technology, Rajasthan, India Dr. Vahid Esmaeelzadeh, University of Science and Technology, Iran Dr. Jacek M. Czerniak, Casimir the Great University in Bydgoszcz, Institute of Technology, Poland Associate Prof. Anisur Rehman Nasir, Jamia Millia Islamia University Assistant Prof. Imran Ahmad, COMSATS Institute of Information Technology, Pakistan Professor Ghulam Qasim, Preston University, Islamabad, Pakistan Dr. Parameshachari B D, GSSS Institute of Engineering and Technology for Women Dr. Wencan Luo, University of Pittsburgh, US Dr. Musa PEKER, Faculty of Technology, Mugla Sitki Kocman University, Turkey Dr. Gunasekaran Shanmugam, Anna University, India Dr. Binh P. Nguyen, National University of Singapore, Singapore Dr. Rajkumar Jain, Indian Institute of Technology Indore, India Dr. Imtiaz Ali Halepoto, QUEST Nawabshah, Pakistan Dr. Shaligram Prajapat, Devi Ahilya University Indore India Dr. Sunita Singhal, Birla Institute of Technologyand Science, Pilani, India Dr. Ijaz Ali Shoukat, King Saud University, Saudi Arabia Dr. Anuj Gupta, IKG Punjab Technical University, India Dr. Sonali Saini, IES-IPS Academy, India Dr. Krishan Kumar, MotiLal Nehru National Institute of Technology, Allahabad, India Dr. Z. Faizal Khan, College of Engineering, Shaqra University, Kingdom of Saudi Arabia Prof. M. Padmavathamma, S.V. University Tirupati, India Prof. A. Velayudham, Cape Institute of Technology, India Prof. Seifeidne Kadry, American University of the Middle East Dr. J. Durga Prasad Rao, Pt. Ravishankar Shukla University, Raipur Assistant Prof. Najam Hasan, Dhofar University Dr. G. Suseendran, Vels University, Pallavaram, Chennai Prof. Ankit Faldu, Gujarat Technological Universiry- Atmiya Institute of Technology and Science Dr. Ali Habiboghli, Islamic Azad University Dr. Deepak Dembla, JECRC University, Jaipur, India Dr. Pankaj Rajan, Walmart Labs, USA Assistant Prof. Radoslava Kraleva, South-West University "Neofit Rilski", Bulgaria Assistant Prof. Medhavi Shriwas, Shri vaishnav institute of Technology, India Associate Prof. Sedat Akleylek, Ondokuz Mayis University, Turkey Dr. U.V. Arivazhagu, Kingston Engineering College Affiliated To Anna University, India Dr. Touseef Ali, University of Engineering and Technology, Taxila, Pakistan Assistant Prof. Naren Jeeva, SASTRA University, India Dr. Riccardo Colella, University of Salento, Italy Dr. Enache Maria Cristina, University of Galati, Romania Dr. Senthil P, Kurinji College of Arts & Science, India

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

Vol. 14 CIC 2016 Special Issue International Journal of Computer Science and Information Security (IJCSIS) https://sites.google.com/site/ijcsis/ ISSN 1947-5500

Dr. Hasan Ashrafi-rizi, Isfahan University of Medical Sciences, Isfahan, Iran Dr. Mazhar Malik, Institute of Southern Punjab, Pakistan Dr. Yajie Miao, Carnegie Mellon University, USA Dr. Kamran Shaukat, University of the Punjab, Pakistan Dr. Sasikaladevi N., SASTRA University, India Dr. Ali Asghar Rahmani Hosseinabadi, Islamic Azad University Ayatollah Amoli Branch, Amol, Iran Dr. Velin Kralev, South-West University "Neofit Rilski", Blagoevgrad, Bulgaria Dr. Marius Iulian Mihailescu, LUMINA - The University of South-East Europe Dr. Sriramula Nagaprasad, S.R.R.Govt.Arts & Science College, Karimnagar, India Prof (Dr.) Namrata Dhanda, Dr. APJ Abdul Kalam Technical University, Lucknow, India Dr. Javed Ahmed Mahar, Shah Abdul Latif University, Khairpur Mir’s, Pakistan Dr. B. Narendra Kumar Rao, Sree Vidyanikethan Engineering College, India Dr. Shahzad Anwar, University of Engineering & Technology Peshawar, Pakistan Dr. Basit Shahzad, King Saud University, Riyadh - Saudi Arabia Dr. Nilamadhab Mishra, Chang Gung University Dr. Sachin Kumar, Indian Institute of Technology Roorkee Dr. Santosh Nanda, Biju-Pattnaik University of Technology Dr. Sherzod Turaev, International Islamic University Malaysia Dr. Yilun Shang, Tongji University, Department of Mathematics, Shanghai, China Dr. Nuzhat Shaikh, Modern Education society's College of Engineering, Pune, India Dr. Parul Verma, Amity University, Lucknow campus, India Dr. Rachid Alaoui, Agadir Ibn Zohr University, Agadir, Morocco Dr. Dharmendra Patel, Charotar University of Science and Technology, India Dr. Dong Zhang, University of Central Florida, USA Dr. Kennedy Chinedu Okafor, Federal University of Technology Owerri, Nigeria Prof. C Ram Kumar, Dr NGP Institute of Technology, India Dr. Sandeep Gupta, GGS IP University, New Delhi, India Dr. Shahanawaj Ahamad, University of Ha'il, Ha'il City, Ministry of Higher Education, Kingdom of Saudi Arabia Dr. Najeed Ahmed Khan, NED University of Engineering & Technology, India Dr. Sajid Ullah Khan, Universiti Malaysia Sarawak, Malaysia Dr. Muhammad Asif, National Textile University Faisalabad, Pakistan Dr. Yu BI, University of Central Florida, Orlando, FL, USA Dr. Brijendra Kumar Joshi, Research Center, Military College of Telecommunication Engineering, India

International Conference on Advances in Computational Intelligence and Communication (CIC 2016) Pondicherry Engineering College, Puducherry, India October 19 & 20 - 2016

CALL FOR PAPERS International Journal of Computer Science and Information Security IJCSIS 2016 ISSN: 1947-5500 http://sites.google.com/site/ijcsis/ International Journal Computer Science and Information Security, IJCSIS, is the premier scholarly venue in the areas of computer science and security issues. IJCSIS 2011 will provide a high profile, leading edge platform for researchers and engineers alike to publish state-of-the-art research in the respective fields of information technology and communication security. The journal will feature a diverse mixture of publication articles including core and applied computer science related topics. Authors are solicited to contribute to the special issue by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to. Submissions may span a broad range of topics, e.g.:

Track A: Security Access control, Anonymity, Audit and audit reduction & Authentication and authorization, Applied cryptography, Cryptanalysis, Digital Signatures, Biometric security, Boundary control devices, Certification and accreditation, Cross-layer design for security, Security & Network Management, Data and system integrity, Database security, Defensive information warfare, Denial of service protection, Intrusion Detection, Anti-malware, Distributed systems security, Electronic commerce, E-mail security, Spam, Phishing, E-mail fraud, Virus, worms, Trojan Protection, Grid security, Information hiding and watermarking & Information survivability, Insider threat protection, Integrity Intellectual property protection, Internet/Intranet Security, Key management and key recovery, Languagebased security, Mobile and wireless security, Mobile, Ad Hoc and Sensor Network Security, Monitoring and surveillance, Multimedia security ,Operating system security, Peer-to-peer security, Performance Evaluations of Protocols & Security Application, Privacy and data protection, Product evaluation criteria and compliance, Risk evaluation and security certification, Risk/vulnerability assessment, Security & Network Management, Security Models & protocols, Security threats & countermeasures (DDoS, MiM, Session Hijacking, Replay attack etc,), Trusted computing, Ubiquitous Computing Security, Virtualization security, VoIP security, Web 2.0 security, Submission Procedures, Active Defense Systems, Adaptive Defense Systems, Benchmark, Analysis and Evaluation of Security Systems, Distributed Access Control and Trust Management, Distributed Attack Systems and Mechanisms, Distributed Intrusion Detection/Prevention Systems, Denial-of-Service Attacks and Countermeasures, High Performance Security Systems, Identity Management and Authentication, Implementation, Deployment and Management of Security Systems, Intelligent Defense Systems, Internet and Network Forensics, Largescale Attacks and Defense, RFID Security and Privacy, Security Architectures in Distributed Network Systems, Security for Critical Infrastructures, Security for P2P systems and Grid Systems, Security in ECommerce, Security and Privacy in Wireless Networks, Secure Mobile Agents and Mobile Code, Security Protocols, Security Simulation and Tools, Security Theory and Tools, Standards and Assurance Methods, Trusted Computing, Viruses, Worms, and Other Malicious Code, World Wide Web Security, Novel and emerging secure architecture, Study of attack strategies, attack modeling, Case studies and analysis of actual attacks, Continuity of Operations during an attack, Key management, Trust management, Intrusion detection techniques, Intrusion response, alarm management, and correlation analysis, Study of tradeoffs between security and system performance, Intrusion tolerance systems, Secure protocols, Security in wireless networks (e.g. mesh networks, sensor networks, etc.), Cryptography and Secure Communications, Computer Forensics, Recovery and Healing, Security Visualization, Formal Methods in Security, Principles for Designing a Secure Computing System, Autonomic Security, Internet Security, Security in Health Care Systems, Security Solutions Using Reconfigurable Computing, Adaptive and Intelligent Defense Systems, Authentication and Access control, Denial of service attacks and countermeasures, Identity, Route and

Location Anonymity schemes, Intrusion detection and prevention techniques, Cryptography, encryption algorithms and Key management schemes, Secure routing schemes, Secure neighbor discovery and localization, Trust establishment and maintenance, Confidentiality and data integrity, Security architectures, deployments and solutions, Emerging threats to cloud-based services, Security model for new services, Cloud-aware web service security, Information hiding in Cloud Computing, Securing distributed data storage in cloud, Security, privacy and trust in mobile computing systems and applications, Middleware security & Security features: middleware software is an asset on its own and has to be protected, interaction between security-specific and other middleware features, e.g., context-awareness, Middleware-level security monitoring and measurement: metrics and mechanisms for quantification and evaluation of security enforced by the middleware, Security co-design: trade-off and co-design between application-based and middleware-based security, Policy-based management: innovative support for policy-based definition and enforcement of security concerns, Identification and authentication mechanisms: Means to capture application specific constraints in defining and enforcing access control rules, Middleware-oriented security patterns: identification of patterns for sound, reusable security, Security in aspect-based middleware: mechanisms for isolating and enforcing security aspects, Security in agent-based platforms: protection for mobile code and platforms, Smart Devices: Biometrics, National ID cards, Embedded Systems Security and TPMs, RFID Systems Security, Smart Card Security, Pervasive Systems: Digital Rights Management (DRM) in pervasive environments, Intrusion Detection and Information Filtering, Localization Systems Security (Tracking of People and Goods), Mobile Commerce Security, Privacy Enhancing Technologies, Security Protocols (for Identification and Authentication, Confidentiality and Privacy, and Integrity), Ubiquitous Networks: Ad Hoc Networks Security, DelayTolerant Network Security, Domestic Network Security, Peer-to-Peer Networks Security, Security Issues in Mobile and Ubiquitous Networks, Security of GSM/GPRS/UMTS Systems, Sensor Networks Security, Vehicular Network Security, Wireless Communication Security: Bluetooth, NFC, WiFi, WiMAX, WiMedia, others

This Track will emphasize the design, implementation, management and applications of computer communications, networks and services. Topics of mostly theoretical nature are also welcome, provided there is clear practical potential in applying the results of such work. Track B: Computer Science Broadband wireless technologies: LTE, WiMAX, WiRAN, HSDPA, HSUPA, Resource allocation and interference management, Quality of service and scheduling methods, Capacity planning and dimensioning, Cross-layer design and Physical layer based issue, Interworking architecture and interoperability, Relay assisted and cooperative communications, Location and provisioning and mobility management, Call admission and flow/congestion control, Performance optimization, Channel capacity modeling and analysis, Middleware Issues: Event-based, publish/subscribe, and message-oriented middleware, Reconfigurable, adaptable, and reflective middleware approaches, Middleware solutions for reliability, fault tolerance, and quality-of-service, Scalability of middleware, Context-aware middleware, Autonomic and self-managing middleware, Evaluation techniques for middleware solutions, Formal methods and tools for designing, verifying, and evaluating, middleware, Software engineering techniques for middleware, Service oriented middleware, Agent-based middleware, Security middleware, Network Applications: Network-based automation, Cloud applications, Ubiquitous and pervasive applications, Collaborative applications, RFID and sensor network applications, Mobile applications, Smart home applications, Infrastructure monitoring and control applications, Remote health monitoring, GPS and location-based applications, Networked vehicles applications, Alert applications, Embeded Computer System, Advanced Control Systems, and Intelligent Control : Advanced control and measurement, computer and microprocessor-based control, signal processing, estimation and identification techniques, application specific IC’s, nonlinear and adaptive control, optimal and robot control, intelligent control, evolutionary computing, and intelligent systems, instrumentation subject to critical conditions, automotive, marine and aero-space control and all other control applications, Intelligent Control System, Wiring/Wireless Sensor, Signal Control System. Sensors, Actuators and Systems Integration : Intelligent sensors and actuators, multisensor fusion, sensor array and multi-channel processing, micro/nano technology, microsensors and microactuators, instrumentation electronics, MEMS and system integration, wireless sensor, Network Sensor, Hybrid

Sensor, Distributed Sensor Networks. Signal and Image Processing : Digital signal processing theory, methods, DSP implementation, speech processing, image and multidimensional signal processing, Image analysis and processing, Image and Multimedia applications, Real-time multimedia signal processing, Computer vision, Emerging signal processing areas, Remote Sensing, Signal processing in education. Industrial Informatics: Industrial applications of neural networks, fuzzy algorithms, Neuro-Fuzzy application, bioInformatics, real-time computer control, real-time information systems, human-machine interfaces, CAD/CAM/CAT/CIM, virtual reality, industrial communications, flexible manufacturing systems, industrial automated process, Data Storage Management, Harddisk control, Supply Chain Management, Logistics applications, Power plant automation, Drives automation. Information Technology, Management of Information System : Management information systems, Information Management, Nursing information management, Information System, Information Technology and their application, Data retrieval, Data Base Management, Decision analysis methods, Information processing, Operations research, E-Business, E-Commerce, E-Government, Computer Business, Security and risk management, Medical imaging, Biotechnology, Bio-Medicine, Computer-based information systems in health care, Changing Access to Patient Information, Healthcare Management Information Technology. Communication/Computer Network, Transportation Application : On-board diagnostics, Active safety systems, Communication systems, Wireless technology, Communication application, Navigation and Guidance, Vision-based applications, Speech interface, Sensor fusion, Networking theory and technologies, Transportation information, Autonomous vehicle, Vehicle application of affective computing, Advance Computing technology and their application : Broadband and intelligent networks, Data Mining, Data fusion, Computational intelligence, Information and data security, Information indexing and retrieval, Information processing, Information systems and applications, Internet applications and performances, Knowledge based systems, Knowledge management, Software Engineering, Decision making, Mobile networks and services, Network management and services, Neural Network, Fuzzy logics, Neuro-Fuzzy, Expert approaches, Innovation Technology and Management : Innovation and product development, Emerging advances in business and its applications, Creativity in Internet management and retailing, B2B and B2C management, Electronic transceiver device for Retail Marketing Industries, Facilities planning and management, Innovative pervasive computing applications, Programming paradigms for pervasive systems, Software evolution and maintenance in pervasive systems, Middleware services and agent technologies, Adaptive, autonomic and context-aware computing, Mobile/Wireless computing systems and services in pervasive computing, Energy-efficient and green pervasive computing, Communication architectures for pervasive computing, Ad hoc networks for pervasive communications, Pervasive opportunistic communications and applications, Enabling technologies for pervasive systems (e.g., wireless BAN, PAN), Positioning and tracking technologies, Sensors and RFID in pervasive systems, Multimodal sensing and context for pervasive applications, Pervasive sensing, perception and semantic interpretation, Smart devices and intelligent environments, Trust, security and privacy issues in pervasive systems, User interfaces and interaction models, Virtual immersive communications, Wearable computers, Standards and interfaces for pervasive computing environments, Social and economic models for pervasive systems, Active and Programmable Networks, Ad Hoc & Sensor Network, Congestion and/or Flow Control, Content Distribution, Grid Networking, High-speed Network Architectures, Internet Services and Applications, Optical Networks, Mobile and Wireless Networks, Network Modeling and Simulation, Multicast, Multimedia Communications, Network Control and Management, Network Protocols, Network Performance, Network Measurement, Peer to Peer and Overlay Networks, Quality of Service and Quality of Experience, Ubiquitous Networks, Crosscutting Themes – Internet Technologies, Infrastructure, Services and Applications; Open Source Tools, Open Models and Architectures; Security, Privacy and Trust; Navigation Systems, Location Based Services; Social Networks and Online Communities; ICT Convergence, Digital Economy and Digital Divide, Neural Networks, Pattern Recognition, Computer Vision, Advanced Computing Architectures and New Programming Models, Visualization and Virtual Reality as Applied to Computational Science, Computer Architecture and Embedded Systems, Technology in Education, Theoretical Computer Science, Computing Ethics, Computing Practices & Applications

Authors are invited to submit papers through e-mail [email protected]. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated by IJCSIS. Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://sites.google.com/site/ijcsis/authors-notes .

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