International Journal of Computer Science Issues

                 

IJCSI

         

 

International Journal of Computer Science Issues

Volume 7, Issue 3, No 11, May 2010 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814

© IJCSI PUBLICATION www.IJCSI.org

IJCSI proceedings are currently indexed by:

© IJCSI PUBLICATION 2010 www.IJCSI.org

IJCSI Publicity Board 2010

Dr. Borislav D Dimitrov Department of General Practice, Royal College of Surgeons in Ireland Dublin, Ireland

Dr. Vishal Goyal Department of Computer Science, Punjabi University Patiala, India

Mr. Nehinbe Joshua University of Essex Colchester, Essex, UK

Mr. Vassilis Papataxiarhis Department of Informatics and Telecommunications National and Kapodistrian University of Athens, Athens, Greece

EDITORIAL In this third edition of 2010, we bring forward issues from various dynamic computer science areas ranging from system performance, computer vision, artificial intelligence, software engineering, multimedia , pattern recognition, information retrieval, databases, security and networking among others. As always we thank all our reviewers for providing constructive comments on papers sent to them for review. This helps enormously in improving the quality of papers published in this issue. IJCSI will maintain its policy of sending print copies of the journal to all corresponding authors worldwide free of charge. Apart from availability of the full-texts from the journal website, all published papers are deposited in open-access repositories to make access easier and ensure continuous availability of its proceedings. The transition from the 2nd issue to the 3rd one has been marked with an agreement signed between IJCSI and ProQuest and EBSCOHOST, two leading directories to help in the dissemination of our published papers. We believe further indexing and more dissemination will definitely lead to further citations of our authors’ articles. We are pleased to present IJCSI Volume 7, Issue 3, May 2010, split in eleven numbers (IJCSI Vol. 7, Issue 3, No. 11). The acceptance rate for this issue is 37.88%. We wish you a happy reading!

IJCSI Editorial Board May 2010 Issue ISSN (Print): 1694-0814 ISSN (Online): 1694-0784 © IJCSI Publications www.IJCSI.org 

IJCSI Editorial Board 2010

Dr Tristan Vanrullen Chief Editor LPL, Laboratoire Parole et Langage - CNRS - Aix en Provence, France LABRI, Laboratoire Bordelais de Recherche en Informatique - INRIA - Bordeaux, France LEEE, Laboratoire d'Esthétique et Expérimentations de l'Espace - Université d'Auvergne, France

Dr Constantino Malagôn Associate Professor Nebrija University Spain

Dr Lamia Fourati Chaari Associate Professor Multimedia and Informatics Higher Institute in SFAX Tunisia

Dr Mokhtar Beldjehem Professor Sainte-Anne University Halifax, NS, Canada

Dr Pascal Chatonnay Assistant Professor MaÎtre de Conférences Laboratoire d'Informatique de l'Université de Franche-Comté Université de Franche-Comté France

Dr Yee-Ming Chen Professor Department of Industrial Engineering and Management Yuan Ze University Taiwan

Dr Vishal Goyal Assistant Professor Department of Computer Science Punjabi University Patiala, India

Dr Natarajan Meghanathan Assistant Professor REU Program Director Department of Computer Science Jackson State University Jackson, USA

Dr Deepak Laxmi Narasimha Department of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

Dr Navneet Agrawal Assistant Professor Department of ECE, College of Technology & Engineering, MPUAT, Udaipur 313001 Rajasthan, India

Prof N. Jaisankar Assistant Professor School of Computing Sciences, VIT University Vellore, Tamilnadu, India

IJCSI Reviewers Committee 2010  Mr. Markus Schatten, University of Zagreb, Faculty of Organization and Informatics, Croatia  Mr. Vassilis Papataxiarhis, Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece  Dr Modestos Stavrakis, University of the Aegean, Greece  Dr Fadi KHALIL, LAAS -- CNRS Laboratory, France  Dr Dimitar Trajanov, Faculty of Electrical Engineering and Information technologies, ss. Cyril and Methodius Univesity - Skopje, Macedonia  Dr Jinping Yuan, College of Information System and Management,National Univ. of Defense Tech., China  Dr Alexis Lazanas, Ministry of Education, Greece  Dr Stavroula Mougiakakou, University of Bern, ARTORG Center for Biomedical Engineering Research, Switzerland  Dr Cyril de Runz, CReSTIC-SIC, IUT de Reims, University of Reims, France  Mr. Pramodkumar P. Gupta, Dept of Bioinformatics, Dr D Y Patil University, India  Dr Alireza Fereidunian, School of ECE, University of Tehran, Iran  Mr. Fred Viezens, Otto-Von-Guericke-University Magdeburg, Germany  Dr. Richard G. Bush, Lawrence Technological University, United States  Dr. Ola Osunkoya, Information Security Architect, USA  Mr. Kotsokostas N.Antonios, TEI Piraeus, Hellas  Prof Steven Totosy de Zepetnek, U of Halle-Wittenberg & Purdue U & National Sun Yat-sen U, Germany, USA, Taiwan  Mr. M Arif Siddiqui, Najran University, Saudi Arabia  Ms. Ilknur Icke, The Graduate Center, City University of New York, USA  Prof Miroslav Baca, Faculty of Organization and Informatics, University of Zagreb, Croatia  Dr. Elvia Ruiz Beltrán, Instituto Tecnológico de Aguascalientes, Mexico  Mr. Moustafa Banbouk, Engineer du Telecom, UAE  Mr. Kevin P. Monaghan, Wayne State University, Detroit, Michigan, USA  Ms. Moira Stephens, University of Sydney, Australia  Ms. Maryam Feily, National Advanced IPv6 Centre of Excellence (NAV6) , Universiti Sains Malaysia (USM), Malaysia  Dr. Constantine YIALOURIS, Informatics Laboratory Agricultural University of Athens, Greece  Mrs. Angeles Abella, U. de Montreal, Canada  Dr. Patrizio Arrigo, CNR ISMAC, italy  Mr. Anirban Mukhopadhyay, B.P.Poddar Institute of Management & Technology, India  Mr. Dinesh Kumar, DAV Institute of Engineering & Technology, India  Mr. Jorge L. Hernandez-Ardieta, INDRA SISTEMAS / University Carlos III of Madrid, Spain  Mr. AliReza Shahrestani, University of Malaya (UM), National Advanced IPv6 Centre of Excellence (NAv6), Malaysia  Mr. Blagoj Ristevski, Faculty of Administration and Information Systems Management - Bitola, Republic of Macedonia  Mr. Mauricio Egidio Cantão, Department of Computer Science / University of São Paulo, Brazil  Mr. Jules Ruis, Fractal Consultancy, The Netherlands

 Mr. Mohammad Iftekhar Husain, University at Buffalo, USA  Dr. Deepak Laxmi Narasimha, Department of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia  Dr. Paola Di Maio, DMEM University of Strathclyde, UK  Dr. Bhanu Pratap Singh, Institute of Instrumentation Engineering, Kurukshetra University Kurukshetra, India  Mr. Sana Ullah, Inha University, South Korea  Mr. Cornelis Pieter Pieters, Condast, The Netherlands  Dr. Amogh Kavimandan, The MathWorks Inc., USA  Dr. Zhinan Zhou, Samsung Telecommunications America, USA  Mr. Alberto de Santos Sierra, Universidad Politécnica de Madrid, Spain  Dr. Md. Atiqur Rahman Ahad, Department of Applied Physics, Electronics & Communication Engineering (APECE), University of Dhaka, Bangladesh  Dr. Charalampos Bratsas, Lab of Medical Informatics, Medical Faculty, Aristotle University, Thessaloniki, Greece  Ms. Alexia Dini Kounoudes, Cyprus University of Technology, Cyprus  Mr. Anthony Gesase, University of Dar es salaam Computing Centre, Tanzania  Dr. Jorge A. Ruiz-Vanoye, Universidad Juárez Autónoma de Tabasco, Mexico  Dr. Alejandro Fuentes Penna, Universidad Popular Autónoma del Estado de Puebla, México  Dr. Ocotlán Díaz-Parra, Universidad Juárez Autónoma de Tabasco, México  Mrs. Nantia Iakovidou, Aristotle University of Thessaloniki, Greece  Mr. Vinay Chopra, DAV Institute of Engineering & Technology, Jalandhar  Ms. Carmen Lastres, Universidad Politécnica de Madrid - Centre for Smart Environments, Spain  Dr. Sanja Lazarova-Molnar, United Arab Emirates University, UAE  Mr. Srikrishna Nudurumati, Imaging & Printing Group R&D Hub, Hewlett-Packard, India  Dr. Olivier Nocent, CReSTIC/SIC, University of Reims, France  Mr. Burak Cizmeci, Isik University, Turkey  Dr. Carlos Jaime Barrios Hernandez, LIG (Laboratory Of Informatics of Grenoble), France  Mr. Md. Rabiul Islam, Rajshahi university of Engineering & Technology (RUET), Bangladesh  Dr. LAKHOUA Mohamed Najeh, ISSAT - Laboratory of Analysis and Control of Systems, Tunisia  Dr. Alessandro Lavacchi, Department of Chemistry - University of Firenze, Italy  Mr. Mungwe, University of Oldenburg, Germany  Mr. Somnath Tagore, Dr D Y Patil University, India  Ms. Xueqin Wang, ATCS, USA  Dr. Borislav D Dimitrov, Department of General Practice, Royal College of Surgeons in Ireland, Dublin, Ireland  Dr. Fondjo Fotou Franklin, Langston University, USA  Dr. Vishal Goyal, Department of Computer Science, Punjabi University, Patiala, India  Mr. Thomas J. Clancy, ACM, United States  Dr. Ahmed Nabih Zaki Rashed, Dr. in Electronic Engineering, Faculty of Electronic Engineering, menouf 32951, Electronics and Electrical Communication Engineering Department, Menoufia university, EGYPT, EGYPT  Dr. Rushed Kanawati, LIPN, France  Mr. Koteshwar Rao, K G Reddy College Of ENGG.&TECH,CHILKUR, RR DIST.,AP, India

 Mr. M. Nagesh Kumar, Department of Electronics and Communication, J.S.S. research foundation, Mysore University, Mysore-6, India  Dr. Ibrahim Noha, Grenoble Informatics Laboratory, France  Mr. Muhammad Yasir Qadri, University of Essex, UK  Mr. Annadurai .P, KMCPGS, Lawspet, Pondicherry, India, (Aff. Pondicherry Univeristy, India  Mr. E Munivel , CEDTI (Govt. of India), India  Dr. Chitra Ganesh Desai, University of Pune, India  Mr. Syed, Analytical Services & Materials, Inc., USA  Dr. Mashud Kabir, Department of Computer Science, University of Tuebingen, Germany  Mrs. Payal N. Raj, Veer South Gujarat University, India  Mrs. Priti Maheshwary, Maulana Azad National Institute of Technology, Bhopal, India  Mr. Mahesh Goyani, S.P. University, India, India  Mr. Vinay Verma, Defence Avionics Research Establishment, DRDO, India  Dr. George A. Papakostas, Democritus University of Thrace, Greece  Mr. Abhijit Sanjiv Kulkarni, DARE, DRDO, India  Mr. Kavi Kumar Khedo, University of Mauritius, Mauritius  Dr. B. Sivaselvan, Indian Institute of Information Technology, Design & Manufacturing, Kancheepuram, IIT Madras Campus, India  Dr. Partha Pratim Bhattacharya, Greater Kolkata College of Engineering and Management, West Bengal University of Technology, India  Mr. Manish Maheshwari, Makhanlal C University of Journalism & Communication, India  Dr. Siddhartha Kumar Khaitan, Iowa State University, USA  Dr. Mandhapati Raju, General Motors Inc, USA  Dr. M.Iqbal Saripan, Universiti Putra Malaysia, Malaysia  Mr. Ahmad Shukri Mohd Noor, University Malaysia Terengganu, Malaysia  Mr. Selvakuberan K, TATA Consultancy Services, India  Dr. Smita Rajpal, Institute of Technology and Management, Gurgaon, India  Mr. Rakesh Kachroo, Tata Consultancy Services, India  Mr. Raman Kumar, National Institute of Technology, Jalandhar, Punjab., India  Mr. Nitesh Sureja, S.P.University, India  Dr. M. Emre Celebi, Louisiana State University, Shreveport, USA  Dr. Aung Kyaw Oo, Defence Services Academy, Myanmar  Mr. Sanjay P. Patel, Sankalchand Patel College of Engineering, Visnagar, Gujarat, India  Dr. Pascal Fallavollita, Queens University, Canada  Mr. Jitendra Agrawal, Rajiv Gandhi Technological University, Bhopal, MP, India  Mr. Ismael Rafael Ponce Medellín, Cenidet (Centro Nacional de Investigación y Desarrollo Tecnológico), Mexico  Mr. Supheakmungkol SARIN, Waseda University, Japan  Mr. Shoukat Ullah, Govt. Post Graduate College Bannu, Pakistan  Dr. Vivian Augustine, Telecom Zimbabwe, Zimbabwe  Mrs. Mutalli Vatila, Offshore Business Philipines, Philipines  Dr. Emanuele Goldoni, University of Pavia, Dept. of Electronics, TLC & Networking Lab, Italy  Mr. Pankaj Kumar, SAMA, India  Dr. Himanshu Aggarwal, Punjabi University,Patiala, India  Dr. Vauvert Guillaume, Europages, France

 Prof Yee Ming Chen, Department of Industrial Engineering and Management, Yuan Ze University, Taiwan  Dr. Constantino Malagón, Nebrija University, Spain  Prof Kanwalvir Singh Dhindsa, B.B.S.B.Engg.College, Fatehgarh Sahib (Punjab), India  Mr. Angkoon Phinyomark, Prince of Singkla University, Thailand  Ms. Nital H. Mistry, Veer Narmad South Gujarat University, Surat, India  Dr. M.R.Sumalatha, Anna University, India  Mr. Somesh Kumar Dewangan, Disha Institute of Management and Technology, India  Mr. Raman Maini, Punjabi University, Patiala(Punjab)-147002, India  Dr. Abdelkader Outtagarts, Alcatel-Lucent Bell-Labs, France  Prof Dr. Abdul Wahid, AKG Engg. College, Ghaziabad, India  Mr. Prabu Mohandas, Anna University/Adhiyamaan College of Engineering, india  Dr. Manish Kumar Jindal, Panjab University Regional Centre, Muktsar, India  Prof Mydhili K Nair, M S Ramaiah Institute of Technnology, Bangalore, India  Dr. C. Suresh Gnana Dhas, VelTech MultiTech Dr.Rangarajan Dr.Sagunthala Engineering College,Chennai,Tamilnadu, India  Prof Akash Rajak, Krishna Institute of Engineering and Technology, Ghaziabad, India  Mr. Ajay Kumar Shrivastava, Krishna Institute of Engineering & Technology, Ghaziabad, India  Mr. Deo Prakash, SMVD University, Kakryal(J&K), India  Dr. Vu Thanh Nguyen, University of Information Technology HoChiMinh City, VietNam  Prof Deo Prakash, SMVD University (A Technical University open on I.I.T. Pattern) Kakryal (J&K), India  Dr. Navneet Agrawal, Dept. of ECE, College of Technology & Engineering, MPUAT, Udaipur 313001 Rajasthan, India  Mr. Sufal Das, Sikkim Manipal Institute of Technology, India  Mr. Anil Kumar, Sikkim Manipal Institute of Technology, India  Dr. B. Prasanalakshmi, King Saud University, Saudi Arabia.  Dr. K D Verma, S.V. (P.G.) College, Aligarh, India  Mr. Mohd Nazri Ismail, System and Networking Department, University of Kuala Lumpur (UniKL), Malaysia  Dr. Nguyen Tuan Dang, University of Information Technology, Vietnam National University Ho Chi Minh city, Vietnam  Dr. Abdul Aziz, University of Central Punjab, Pakistan  Dr. P. Vasudeva Reddy, Andhra University, India  Mrs. Savvas A. Chatzichristofis, Democritus University of Thrace, Greece  Mr. Marcio Dorn, Federal University of Rio Grande do Sul - UFRGS Institute of Informatics, Brazil  Mr. Luca Mazzola, University of Lugano, Switzerland  Mr. Nadeem Mahmood, Department of Computer Science, University of Karachi, Pakistan  Mr. Hafeez Ullah Amin, Kohat University of Science & Technology, Pakistan  Dr. Professor Vikram Singh, Ch. Devi Lal University, Sirsa (Haryana), India  Mr. M. Azath, Calicut/Mets School of Enginerring, India  Dr. J. Hanumanthappa, DoS in CS, University of Mysore, India  Dr. Shahanawaj Ahamad, Department of Computer Science, King Saud University, Saudi Arabia  Dr. K. Duraiswamy, K. S. Rangasamy College of Technology, India  Prof. Dr Mazlina Esa, Universiti Teknologi Malaysia, Malaysia

 Dr. P. Vasant, Power Control Optimization (Global), Malaysia  Dr. Taner Tuncer, Firat University, Turkey  Dr. Norrozila Sulaiman, University Malaysia Pahang, Malaysia  Prof. S K Gupta, BCET, Guradspur, India  Dr. Latha Parameswaran, Amrita Vishwa Vidyapeetham, India  Mr. M. Azath, Anna University, India  Dr. P. Suresh Varma, Adikavi Nannaya University, India  Prof. V. N. Kamalesh, JSS Academy of Technical Education, India  Dr. D Gunaseelan, Ibri College of Technology, Oman  Mr. Sanjay Kumar Anand, CDAC, India  Mr. Akshat Verma, CDAC, India  Mrs. Fazeela Tunnisa, Najran University, Kingdom of Saudi Arabia  Mr. Hasan Asil, Islamic Azad University Tabriz Branch (Azarshahr), Iran  Prof. Dr Sajal Kabiraj, Fr. C Rodrigues Institute of Management Studies (Affiliated to University of Mumbai, India), India  Mr. Syed Fawad Mustafa, GAC Center, Shandong University, China  Dr. Natarajan Meghanathan, Jackson State University, Jackson, MS, USA  Prof. Selvakani Kandeeban, Francis Xavier Engineering College, India  Mr. Tohid Sedghi, Urmia University, Iran  Dr. S. Sasikumar, PSNA College of Engg and Tech, Dindigul, India  Dr. Anupam Shukla, Indian Institute of Information Technology and Management Gwalior, India  Mr. Rahul Kala, Indian Institute of Inforamtion Technology and Management Gwalior, India  Dr. A V Nikolov, National University of Lesotho, Lesotho  Mr. Kamal Sarkar, Department of Computer Science and Engineering, Jadavpur University, India  Dr. Mokhled S. AlTarawneh, Computer Engineering Dept., Faculty of Engineering, Mutah University, Jordan, Jordan  Prof. Sattar J Aboud, Iraqi Council of Representatives, Iraq-Baghdad  Dr. Prasant Kumar Pattnaik, Department of CSE, KIST, India  Dr. Mohammed Amoon, King Saud University, Saudi Arabia  Dr. Tsvetanka Georgieva, Department of Information Technologies, St. Cyril and St. Methodius University of Veliko Tarnovo, Bulgaria  Dr. Eva Volna, University of Ostrava, Czech Republic  Mr. Ujjal Marjit, University of Kalyani, West-Bengal, India  Dr. Prasant Kumar Pattnaik, KIST,Bhubaneswar,India, India  Dr. Guezouri Mustapha, Department of Electronics, Faculty of Electrical Engineering, University of Science and Technology (USTO), Oran, Algeria  Mr. Maniyar Shiraz Ahmed, Najran University, Najran, Saudi Arabia  Dr. Sreedhar Reddy, JNTU, SSIETW, Hyderabad, India  Mr. Bala Dhandayuthapani Veerasamy, Mekelle University, Ethiopa  Mr. Arash Habibi Lashkari, University of Malaya (UM), Malaysia  Mr. Rajesh Prasad, LDC Institute of Technical Studies, Allahabad, India  Ms. Habib Izadkhah, Tabriz University, Iran  Dr. Lokesh Kumar Sharma, Chhattisgarh Swami Vivekanand Technical University Bhilai, India  Mr. Kuldeep Yadav, IIIT Delhi, India  Dr. Naoufel Kraiem, Institut Superieur d'Informatique, Tunisia

 Prof. Frank Ortmeier, Otto-von-Guericke-Universitaet Magdeburg, Germany  Mr. Ashraf Aljammal, USM, Malaysia  Mrs. Amandeep Kaur, Department of Computer Science, Punjabi University, Patiala, Punjab, India  Mr. Babak Basharirad, University Technology of Malaysia, Malaysia  Mr. Avinash singh, Kiet Ghaziabad, India  Dr. Miguel Vargas-Lombardo, Technological University of Panama, Panama  Dr. Tuncay Sevindik, Firat University, Turkey  Ms. Pavai Kandavelu, Anna University Chennai, India  Mr. Ravish Khichar, Global Institute of Technology, India  Mr Aos Alaa Zaidan Ansaef, Multimedia University, Cyberjaya, Malaysia  Dr. Awadhesh Kumar Sharma, Dept. of CSE, MMM Engg College, Gorakhpur-273010, UP, India  Mr. Qasim Siddique, FUIEMS, Pakistan  Dr. Le Hoang Thai, University of Science, Vietnam National University - Ho Chi Minh City, Vietnam  Dr. Saravanan C, NIT, Durgapur, India  Dr. Vijay Kumar Mago, DAV College, Jalandhar, India  Dr. Do Van Nhon, University of Information Technology, Vietnam  Mr. Georgios Kioumourtzis, University of Patras, Greece  Mr. Amol D.Potgantwar, SITRC Nasik, India  Mr. Lesedi Melton Masisi, Council for Scientific and Industrial Research, South Africa  Dr. Karthik.S, Department of Computer Science & Engineering, SNS College of Technology, India  Mr. Nafiz Imtiaz Bin Hamid, Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Bangladesh  Mr. Muhammad Imran Khan, Universiti Teknologi PETRONAS, Malaysia  Dr. Abdul Kareem M. Radhi, Information Engineering - Nahrin University, Iraq  Dr. Mohd Nazri Ismail, University of Kuala Lumpur, Malaysia  Dr. Manuj Darbari, BBDNITM, Institute of Technology, A-649, Indira Nagar, Lucknow 226016, India  Ms. Izerrouken, INP-IRIT, France  Mr. Nitin Ashokrao Naik, Dept. of Computer Science, Yeshwant Mahavidyalaya, Nanded, India  Mr. Nikhil Raj, National Institute of Technology, Kurukshetra, India  Prof. Maher Ben Jemaa, National School of Engineers of Sfax, Tunisia  Prof. Rajeshwar Singh, BRCM College of Engineering and Technology, Bahal Bhiwani, Haryana, India  Mr. Gaurav Kumar, Department of Computer Applications, Chitkara Institute of Engineering and Technology, Rajpura, Punjab, India  Mr. Ajeet Kumar Pandey, Indian Institute of Technology, Kharagpur, India  Mr. Rajiv Phougat, IBM Corporation, USA  Mrs. Aysha V, College of Applied Science Pattuvam affiliated with Kannur University, India  Dr. Debotosh Bhattacharjee, Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, India  Dr. Neelam Srivastava, Institute of engineering & Technology, Lucknow, India  Prof. Sweta Verma, Galgotia's College of Engineering & Technology, Greater Noida, India  Mr. Harminder Singh BIndra, MIMIT, INDIA  Dr. Lokesh Kumar Sharma, Chhattisgarh Swami Vivekanand Technical University, Bhilai, India  Mr. Tarun Kumar, U.P. Technical University/Radha Govinend Engg. College, India  Mr. Tirthraj Rai, Jawahar Lal Nehru University, New Delhi, India

 Mr. Akhilesh Tiwari, Madhav Institute of Technology & Science, India  Mr. Dakshina Ranjan Kisku, Dr. B. C. Roy Engineering College, WBUT, India  Ms. Anu Suneja, Maharshi Markandeshwar University, Mullana, Haryana, India  Mr. Munish Kumar Jindal, Punjabi University Regional Centre, Jaito (Faridkot), India  Dr. Ashraf Bany Mohammed, Management Information Systems Department, Faculty of Administrative and Financial Sciences, Petra University, Jordan  Mrs. Jyoti Jain, R.G.P.V. Bhopal, India  Dr. Lamia Chaari, SFAX University, Tunisia  Mr. Akhter Raza Syed, Department of Computer Science, University of Karachi, Pakistan  Prof. Khubaib Ahmed Qureshi, Information Technology Department, HIMS, Hamdard University, Pakistan  Prof. Boubker Sbihi, Ecole des Sciences de L'Information, Morocco  Dr. S. M. Riazul Islam, Inha University, South Korea  Prof. Lokhande S.N., S.R.T.M.University, Nanded (MH), India  Dr. Vijay H Mankar, Dept. of Electronics, Govt. Polytechnic, Nagpur, India  Dr. M. Sreedhar Reddy, JNTU, Hyderabad, SSIETW, India  Mr. Ojesanmi Olusegun, Ajayi Crowther University, Oyo, Nigeria  Ms. Mamta Juneja, RBIEBT, PTU, India  Dr. Ekta Walia Bhullar, Maharishi Markandeshwar University, Mullana Ambala (Haryana), India  Prof. Chandra Mohan, John Bosco Engineering College, India  Mr. Nitin A. Naik, Yeshwant Mahavidyalaya, Nanded, India  Mr. Sunil Kashibarao Nayak, Bahirji Smarak Mahavidyalaya, Basmathnagar Dist-Hingoli., India  Prof. Rakesh.L, Vijetha Institute of Technology, Bangalore, India  Mr B. M. Patil, Indian Institute of Technology, Roorkee, Uttarakhand, India  Mr. Thipendra Pal Singh, Sharda University, K.P. III, Greater Noida, Uttar Pradesh, India  Prof. Chandra Mohan, John Bosco Engg College, India  Mr. Hadi Saboohi, University of Malaya - Faculty of Computer Science and Information Technology, Malaysia  Dr. R. Baskaran, Anna University, India  Dr. Wichian Sittiprapaporn, Mahasarakham University College of Music, Thailand  Mr. Lai Khin Wee, Universiti Teknologi Malaysia, Malaysia  Dr. Kamaljit I. Lakhtaria, Atmiya Institute of Technology, India  Mrs. Inderpreet Kaur, PTU, Jalandhar, India  Mr. Iqbaldeep Kaur, PTU / RBIEBT, India  Mrs. Vasudha Bahl, Maharaja Agrasen Institute of Technology, Delhi, India  Prof. Vinay Uttamrao Kale, P.R.M. Institute of Technology & Research, Badnera, Amravati, Maharashtra, India  Mr. Suhas J Manangi, Microsoft, India  Ms. Anna Kuzio, Adam Mickiewicz University, School of English, Poland  Dr. Debojyoti Mitra, Sir Padampat Singhania University, India  Prof. Rachit Garg, Department of Computer Science, L K College, India  Mrs. Manjula K A, Kannur University, India  Mr. Rakesh Kumar, Indian Institute of Technology Roorkee, India

TABLE OF CONTENTS

1. Efficient Algorithm for Redundant Reader Elimination in Wireless RFID Networks Nazish Irfan, Mustapha C.E. Yagoub

Pg 1-8

2. On One Approach to Scientific CAD/CAE Software Developing Process George Sergia, Alexander Demurov, George Petrosyan, Roman Jobava

Pg 9-15

3. Modeling Throughput Performance in 802.11 WLAN Moses Ekpenyong, Joseph Isabona

Pg 16-22

4. Hello Flood Attack and its Countermeasures in Wireless Sensor Networks Virendra Pal Singh, Sweta Jain, Jyoti Singhai

Pg 23-27

5. Faster and Efficient Web Crawling with Parallel Migrating Web Crawler Akansha Singh, Krishna Kant Singh

Pg 28-32

6. The morphological analysis of Arabic verbs by using the surface patterns A. Yousfi

Pg 33-36

7. Real-Time Video Streaming Over Bluetooth Network Between Two Mobile Nodes Sourav Banerjee, Dipansu Mondal, Sumit Das, Ramendu Bikash Guin

Pg 37-39

8. A New Semantic Similarity Metric for Solving Sparse Data Problem in Ontology based Information Retrieval System K. Saruladha, G. Aghila, Sajina Raj

Pg 40-48

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814

1

Efficient Algorithm for Redundant Reader Elimination in Wireless RFID Networks Nazish Irfan and Mustapha C.E. Yagoub School of Information Technology and Engineering (SITE), University of Ottawa, Ottawa, Ontario, Canada

Abstract Radio Frequency Identification (RFID) systems, due to recent technological advances, are being deployed in large scale for different applications. However, this requires a dense deployment of readers to cover the working area. Without optimizing reader's distribution and number, many of the readers will be redundant, reducing the efficiency of the whole RFID system. The problem of eliminating redundant readers has motivated researchers to propose different algorithms and optimization techniques. In this paper, the authors presented a new and efficient redundant reader elimination technique based on weights associated with reader's neighbor and coverage. Simulation results demonstrate that the proposed algorithm eliminates more redundant readers than those of other wellknown techniques like Redundant Reader Elimination (RRE), Layered Elimination Optimization (LEO) and LEO+RRE algorithms while preserving the coverage ratio quite close to those obtained by RRE, LEO and LEO+RRE.

Keywords: Large Scale, LEO, Reader, Redundancy, RFID, RRE.

1. Introduction Radio Frequency Identification (RFID) is based on radio communication for tagging and identifying an object [1]. It consists of two blocks namely, RFID transceivers (readers) and RFID transponders (tags). The RFID tag consists of a small integrated circuit for storing information and an antenna for communication. A basic RFID system is based on wireless communication between a reader and a tag. RFID readers can read information stored in no-line-of- sight RFID tags and communicate information to central database system through wired or wireless interface [2]. Over the last few years, RFID has drawn a great deal of attention and now is widely believed that RFID can bring revolutionary changes [3]. Indeed, applications of RFID systems include supply chain automation, security and access control, cold chain management (temperature logging) and identification of products at check-out points, to name a few. Some of the major retailors have already invested significantly in RFID

and mandated their manufacturers to place tags on cases and pallets, which resulted in mass production of inexpensive RFID tags [4]. Integration of RFID systems with wireless sensors has broadened the scope of RFID applications. RFID tags can be interfaced with external sensors such as shock, temperature, and light sensors. Similar to wireless sensor networks, RFID systems can be deployed on-line instead of pre-installed statically [2]. To accurately monitor the area of interest, dense deployment of RFID readers and tags is sometimes required. However, this dense deployment of RFID systems in large scale results in unwanted effects. In fact, when multiple readers share the same working environment and communicate over shared wireless channels, a signal from one reader may reach other readers and cause frequency interference. This frequency interference occurs when a reader transmits the communication signal to read a tag and its signal interferes with signals from other readers who are trying to read the same tag. A reader may also interfere with other reader's operation even if the interrogation zones do not overlap because the back-scattered signal from a tag is weak enough to be easily affected by any interference. Thus, signals transmitted from distant readers may be strong enough to hamper accurate decoding of the communication signals back-scattered from adjacent tags. Therefore, frequency interference in the interrogation zones results into inaccurate reads and long reading intervals. Hence, the effect of reader interference on the RFID interrogation range should be analyzed before any large scale deployment of readers in a RFID system [5, 6]. Indeed, unnecessary readers in the network may consume power which can be wasteful. Therefore, finding redundant readers is of great importance for an optimal deployment of a large-scale RFID network. This ensures a user that the minimum number of readers should be used to cover all the tags in a specified zone.

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org The problem of redundant reader elimination has been studied extensively in [2, 7, 8, 9]. In this paper, we proposed an efficient redundant reader elimination algorithm based on weights assigned to reader's neighbor and coverage. In this algorithm, a reader that has more neighbors and minimum or no coverage is a potential candidate for elimination. To validate the performance of the proposed technique, we have also implemented other well-known methods like RRE [2] and LEO [7]. The proposed technique’s performance proves that more redundant readers are removed than those of RRE, LEO and LEO+RRE. The remainder of this paper is organized as follows: Section 2 examines the existing redundant reader elimination techniques and presents a brief survey of related works. Section 3 details the proposed algorithm. Section 4 presents results and discussions. Finally, Section 5 concludes the proposed work.

2. Related Work During the last decade, the RFID collision problem has been extensively covered in literature. It can be categorized as reader to reader interference or reader to tag interference. Reader to reader interference occurs when the interrogation zones of two readers intersect and interfere with each other. Two readers may also interfere each other even if their interrogation zones do not overlap. This interference is due to the use of wireless radio frequencies for communication. Reader to tag interference occurs when more than one reader try to read the same tag simultaneously. In this type of interference, each reader may believe that it is the only reader communicating with the tag while the tag, in fact, is communicating with multiple readers at the same time. The reader collision problem not only results in incorrect operation but also results in reduction of overall read rate of the RFID system [6, 10, 11]. To separate the individual participant signal from one another, many procedures have been developed. Basically, there are four main procedures namely, the Carrier Sense Multiple Access (CSMA), the Frequency Domain Multiple Access (FDMA), the Time Domain Multiple Access (TDMA) and the Code Division Multiple Access (CDMA) [12]. CSMA enables individual data transmission by detecting whether the communication medium is busy. In CSMA, the interrogation zones of two readers do not overlap. However, the signals at particular tag from two readers can interfere each other that make carrier sensing ineffective in the RFID network. FDMA relates to techniques in which several transmission channels on

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various carrier frequencies are simultaneously available to the communicating participants. Since RFID tags do not have a frequency tuning circuit, tags cannot select particular frequency for communication. It can be achieved by the addition of a frequency tuning circuit, which adds to the cost of the RFID system. TDMA relates to techniques in which the entire available channel capacity is divided among the participants chronologically. In TDMA technique, each reader is allocated different time slot to avoid simultaneous transmissions. In a dynamic RFID system, time slot should be reshuffled adaptively to get better read rate. In case of mobility, reader may come closer and start interfering. CDMA uses spread spectrum modulation techniques based on pseudo random codes to spread the data over the entire system. To implement CDMA, a tag requires extra circuitry which will increase its cost. Moreover, assignment of codes to all tags at the development site may be complicated. Therefore, CDMA may not be a cost effective solution. There are many algorithms, which cover reader collision problem available in literature [11, 13, 14, 15, 16]. Colorwave [13] is a TDMA based distributed algorithm with no guaranteed method of communication between neighboring nodes. In this technique, each reader monitors the percentage of successful transmissions and this procedure also assumes that the readers are able to detect collision in the RFID system. HiQ [14] is an online algorithm based on Q-learning to solve the reader collision problem. Q-learning is a form of reinforcement learning, which allocates resources to maximize the number of readers communicating at a single time period. At the same time, it also minimizes the number of collisions among communicating readers. The Pulse [11] is a distributed algorithm based on a beaconing mechanism in which a specific reader while reading a tag periodically broadcasts a beacon on a separate control channel. Any other readers in the network sense the control channel for a beacon before it starts communicating with the tag. If a reader does not receive any beacon at a given time, it starts transmitting a beacon and begins communicating with the tag. This process is expected to achieve fairness among all readers. The DiCa [15] is a distributed and an energy efficient anticollision algorithm similar to the Pulse. The DiCa contains both a data channel and a control channel. Each reader contends through the control channel for the use of data channel. The reader who wins reads the tags through the data channel. This algorithm adjusts the control channel’s range at twice the radius from the first reader to address the hidden and exposed terminal problem. The DiCa algorithm consumes less dissipated energy than that of CSMA, ALOHA and Pulse algorithms. The Gentle [16] is

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org a CSMA based protocol that uses RFID multi-channel and beacon messages to mitigate reader collision. In this algorithm, readers can also put tag information in their beacon messages in order to forward the information to their close readers. Therefore, readers using Gentle algorithm can avoid reader collision more efficiently and reduce waiting time to get tag’s information. Another approach to avoid collision is to reduce the number of redundant readers in the RFID network. In a RFID network, a reader is redundant if all of its tags are also covered by at least one of the other readers in the network. Figure (1) shows a typical example of the redundant reader in a RFID network. It consists of three

Fig. 1 Redundant reader example in RFID network.

readers R1 to R3 and five tags T1 to T5. The tags T2, T3 and T4 that are covered by R2 are also covered by R1 and R3, respectively. Therefore, R2 is a redundant reader, which can be safely removed without violating the full coverage of tags. Eliminating redundant readers from a RFID network has two-fold advantages; First, it increases the lifetime of the overall RFID network by saving the wasteful power used by redundant readers. Second, it improves the RFID network service quality by alleviating the interference among readers. A simple approach to remove redundant reader is that all readers broadcast query messages simultaneously to all tags in their interrogation zones. Each tag may reply by signaling its ID. So, if a reader covers no tags in its interrogation zone or receives no reply from any tags due to reader colllision, it may be called as a redundant reader. There are some major drawbacks to the above approach. First, it requires strict time synchronization among readers, which is not practical in most RFID systems. Second, by turning all redundant readers, the network coverage may be violated.

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The redundant reader elimination (RRE) problem was first introduced by [2]. The RRE algorithm is based on greedy method. The main idea of this algorithm is to record “tag count", i.e., the number of tags a reader covers into RFID tag’s memory. The reader, which has the maximum number of tag-count, will be the holder of the corresponding tag. This procedure iterates the above steps until all the tags in a network are assigned to readers. Finally, readers with no tags assigned are eliminated as redundant readers. In [7], the authors illustrated that RRE algorithm failed to eliminate redundant readers from some specific RFID network topologies. Therefore, they introduced the LEO algorithm, which uses a layered approach. The term "layered" represents the relationship between early query readers and later query readers. The later query readers may have higher probability to be a redundant reader. The fundamental approach of this procedure is "first read first own". In a RFID network, all readers send command signals to RFID tags in their coverage zones to get the record of the tags. The reader that first sends its signal is the owner of the tag. If this tag already has other reader ID as its owner, then tags ID cannot be changed. Finally, the readers in the network with no tags in their coverage zones are eliminated as redundant readers. The authors have also shown that LEO and RRE algorithms can be combined for better performance. In LEO+RRE scheme, first LEO is implemented to eliminate redundant readers. Then, for all the remaining readers, RRE is implemented to eliminate some more redundant readers. The authors have shown that the LEO algorithm can reduce the number of readings and writings effectively. On the other hand, the LEO procedure determines the owner of the tags in a random way. Therefore, the quality of owner selection for a tag is unreliable. Moreover, if a wrong reader is eliminated from the RFID network in the beginning, it may cause unsatisfactory results. In [8], the authors have proposed an algorithm, which takes the advantage of the concept of neighboring reader density to assess the priority of reading. In this algorithm, the priority value of a reader depends on the number of its neighboring readers. Two readers are considered neighbors when they have at least one tag covered by both the two readers. At first step, all readers in the RFID network send commands to tags in their interrogation zones to read them. The readers then increase the reader_num stored in the tag’s memory by 1 and write their IDs as well as the new value of reader_num into the covered tags. Secondly, all readers communicate with tags to obtain the number of neighboring readers and holder information by virtue of reader_num and Rid (reader ID) respectively. Then, each reader calculates the priority in

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org terms of reader_num and writes its ID as a new owner according to the priority comparison. Finally, any reader owing no tag is eliminated as a redundant reader. The density based algorithm works on "first arrive first serve" methodology, i.e., the time delay required by a reader to read a tag defines the priority of that reader among its neighbors. Consider a simple scenario in a RFID network with readers R1, R2 and tags T1, T2. If the delay time in reading a tag T1 by the reader R1 is smaller than R2, then R1 owns T1. However, if R2 reads both T1 and T2, but the delay time in reading T1 is greater than R1, R2 can not own T1. In this way, both readers are kept in the network. In our proposed work, since both coverage and neighbors are taken into consideration, R2 will own T1 and T2, whereas R1 will be eliminated. In [9], the authors have proposed the algorithm TREE which is very similar to LEO. In TREE, reader Ri , with its identifier, sends query packet to all tags in its interrogation zone. When tags respond to the query message, the stored reader identifier is returned by them. The tag can respond to a query of a reader with two possible replies i.e. it may reply NULL reader identifier or stored reader identifier. If a NULL reader identifier is returned by the tag, it indicates that the tag is not identified by other readers and the tag writes the reader identifier Ri on it. If the tag returns identifier Rk and Rk ≠ Ri , then the reader Ri will ignore this query. In this algorithm, if a reader identifier Ri never receives the tag’s response as a NULL identifier, this reader is redundant and will be eliminated from the network.

 

Reader coordinates are easily available. Coverage information i.e. the number of tags each reader has covered in initial round can easily be obtained by data processing subsystem.

It can be noted that the second assumption of collecting coverage information i.e. the total number of tags covered by each reader at central host system does not require new setup to RFID systems. Indeed, such processing system is already included in existing RFID setup. Therefore, this assumption adds no extra cost to the RFID systems. The normal read range for a 1W reader to read a passive tag whose IC consumes about 10 - 30µW to operate when being read is about 3 meters [17]. Since proposed work is based on the number of neighbors to a reader, the neighbor is defined as: Reader A is a neighbor to reader B if (d>0 meter & d< 2D meters), where d is the distance between readers A and B whereas D is the read range of a reader. Total weights assigned to a reader are a function of cost functions and multiplication factor. Cost function of a reader is defined in terms of its coverage and the number of neighbors. Cost function of a reader due to its coverage and number of neighbors is given by Eq. (1) and (2) respectively. The equations are as follows:

r

fc 



(neighbor (r )) (neighbor ( ))

R

fn  1 

coverage ( ) ︵ coverage ( ))

R

In any arbitrary RFID network, any reader that covers more tags and has fewer number of neighbor readers must be given priority. A reader with more neighbors has higher probability of getting its operation interfered by the neighbor readers. It is known that a reader interferes other reader's operations if it intersects each others interrogation zones. Even though, readers do not overlap other reader's interrogation zones, they can still interfere [6]. Therefore, selecting readers with fewer numbers of neighbors will have higher probability of not interfering one another and results in an efficient working of RFID system.

Some of the assumptions of the proposed technique are:

x a m

3. Proposed Algorithm

With above stated fact, the proposed algorithm assigns weights to each reader based on its number of neighbors and the number of covered tags. In this way, the algorithm ensures that the best possible readers are selected for the efficient working of any RFID network.

x a m

Similar to LEO algorithm, TREE also works on the principle of “first read first own”. Since TREE has fewer write operations, it reduces the time and communication complexity than that of RRE. As TREE and LEO works in a similar manner, the shortcomings of LEO mentioned above are also applicable to TREE.

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(1)

(2)

where r defines each individual reader in a network and R is the list of all readers in the network with their individual tag counts and neighbor counts, respectively. A userdefined multiplication factor α, usually between 1 and 3, is used so that the cost functions due to coverage and neighbors are in proportion and can influence each other.

TWreader  lc  f c  ln  f n

(3)

where lc and ln are the load factors assigned to a cost function of a reader for coverage fc and the number of

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org neighbors fn, respectively. Load factors lc and ln are user defined that satisfy the criteria lc + ln = 1. Basic operations of the proposed work can be summarized as follows: 1. 2. 3.

4.

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After insuring that the coverage of the proposed algorithm is very close with the other algorithms, the number of redundant readers eliminated by each algorithm was compared. Figure (3) demonstrates that the proposed

All readers in the RFID network send commands to all tags in their interrogation zones. Each reader coverage information is sent to the central host station, i.e., how many tags (with IDs) each reader has read. For each tag in the RFID network, the proposed algorithm checks how many readers have read it. Further, the algorithm compares the weights of readers that have read the tag. The reader having the maximum weight owns the tag. All the readers of the network with no assigned tags are eliminated as redundant readers.

After eliminating the redundant readers with no assigned tags, the algorithm switches to its second part which is optimization of the network. In the optimization mode, the algorithm picks a reader from the remaining readers based on minimum coverage and maximum neighbors and then eliminate it. Based on the number of readers left and total tags covered by the remaining readers, the algorithm again assigns weigts using Eq. (1), (2) and (3) respectively. Further, the algorithm follows step 2 of its operation to reorder the readers based on total weights assigned to each remaining reader. The procedure iterates until all readers have a number of neighbors equals or less than 3.

Fig. 2 Comparison of coverage of the proposed technique vs. RRE, LEO and LEO+RRE (first setup).

4. Simulation Setups and Results 4.1 Simulation Setups To evaluate the performance of the proposed redundant reader elimination technique, we implemented two experimental setups. In the first setup, we demonstrated the performance of the proposed algorithm in a similar type of experimental setup as presented in [2, 7, 8]. The experimental area of 100 × 100 sqm was taken with 500 readers placed randomly and the numbers of tags were increased from 1000 to 4000. Initially, we compared the maximum coverage obtained by all remaining readers in the RFID network. This step was undertaken to ensure that the coverage attained by the proposed work is in close relation with RRE, LEO and LEO+RRE algorithms. Figure (2) shows the comparison of coverages obtained by proposed technique to RRE, LEO and LEO+RRE. It can be easily observed that the obtained coverage relates very well with the above algorithms.

Fig. 3 Performance comparison of the proposed technique vs. RRE, LEO and LEO+RRE (First setup).

algorithm eliminates more redundant readers than those of RRE, LEO and LEO+RRE. We obtained these results by varying the number of neighbor readers from 0 to 8 for any particular reader. In fact, for any particular reader, more number of neighbor readers could be required because the considered experimental RFID network is very dense. In the second setup, we implemented RFID networks of different sizes. We took five different experimental areas in which the location of readers and tags were randomly

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org generated. When random locations for readers and tags in a RFID network were generated, it was ensured that no reader or tag was located at the same position. Table (1) shows the parameters selected for simulation. Figure (4) shows one of the experimental areas taken in second setup i.e. the area of 55 × 55 sqm , 100 readers and 334 tags.

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readers eliminated by our work are (26.09 - 78.26 %) more than RRE, (29.85 - 62.5 %) more than LEO and (8.75 - 21.05 %) more than LEO+RRE.

Table 1: Parameters for experimental area

Working Area (Square Meter) 30 x 30 55 x 55 75 x 75 85 x 85 100 x 100

Number of Readers 50 100 150 200 250

Number of Tags 200 334 475 662 775

Fig. 5 Performance comparison of the proposed technique vs. RRE, LEO and LEO+RRE (setup A).

Fig. 4 Network topology of a RFID network.

4.2 Results and Discussions In this section, we will discuss the performance of our algorithm and compare our results with those obtained by the state-of-the-art approaches such as RRE, LEO and LEO+RRE. Performance evaluation of proposed work was done in two different ways:  In setup A, performance of proposed work was evaluated for different experimental areas as shown in Table (1).  In setup B, the same experimental area was kept fixed but the read range of readers was varied. In setup A, we compared the number of redundant readers eliminated by our algorithm to those obtained by the other existing techniques. Figure (5) shows that the number of redundant readers eliminated by the proposed procedure outperforms the other compared algorithms. Redundant

Read range is one of the key parameters that define the performance of passive RFID systems. This is the maximum distance at which the power received by the transponder is strong enough to work efficiently. It is also the maximum distance at which the backscattered power received by the reader is strong enough to be detected and properly demodulated [18]. To evaluate the effect of read range on a RFID network, second setup was implemented selecting the experimental area of 55 ×55 sq m, 100 readers and 334 tags. In setup B, the number of redundant readers eliminated by the proposed work is compared to RRE, LEO and LEO+RRE. It is clear from figure (6) that the number of redundant readers eliminated by the proposed procedure outperforms the other algorithms. Redundant readers eliminated by our work are (42.85 - 78.26 %) more than RRE, (36.67 - 68.75 %) more than LEO and (17.14 42.22 %) more than LEO+RRE. Furthermore, all redundant reader elimination techniques presented in literature have many read-write operations [2, 7, 8, 9]. The LEO procedure presented in [7] has minimum write operations (reader writing or updating information on the tag) and the density based procedure [8] has the maximum write operations. Compared to other algorithms, our work has no write operation and has only one read operation.

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org

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probability of interfering will be lower since readers do not have much neighbor readers to interfere with. We have taken 3 as a lower bound number to ensure that the coverage of tags in a RFID network may not be reduced below appreciable limit.

5. Conclusions

Fig. 6 Performance comparison of the proposed technique vs. RRE, LEO and LEO+RRE (setup B).

Since the existing algorithms [2, 7, 8, 9] require write operations, these procedures are only suitable for tags which have both read and write options. There are mostly three types of tags namely, passive, active and semi-active. Passive tags are lower functionality tags that take power from reader for its operation. Active and semi-active tags have batteries to provide power to tag's operation. Passive Read-Only tags are similar to bar codes in that once they are programmed by a product manufacturer cannot be altered. Read-Write tags are often called "smart" tags. These tags give users much more flexibility than ReadOnly tags. These tags can store large amount of data and have an addressable memory, which can be easily changed. Data on Read-Write tag can be erased and re-written thousands of times [19]. Read-Write operations are only available on tags of EPC Class-2 generation and above [20]. However, since tags have extra-functionality i.e. support read-write operations, which adds to the cost of RFID system. Since proposed work requires no write operations, it is suitable for any type of tags. RRE procedure focuses on the maximum number of tags each reader covers for its operation. The LEO and TREE algorithms works with first read first own basis whereas density based procedure focuses on the number of each reader’s neighbor for its operation. In contrast, our work takes total tags covered by each reader, i.e. individual coverage and the number of each reader’s neighbors. The advantage of this approach is that it not only eliminates the maximum possible redundant readers but also optimizes the RFID network for an efficient operation. In the proposed technique, maximum neighbors for any reader in a RFID networks is set equal or less than 3. The

In this paper, we presented a redundant reader elimination technique based on weights related to reader's neighbor and coverage. This algorithm optimizes the RFID network by giving importance to a reader who has fewer numbers of neighbors and more coverage. This work can be used by any arbitrary RFID network. The proposed work needs only one read and no write operation. The simulation results had proved that the proposed algorithm outperformed other state of the art techniques presented in literature such as RRE, LEO and LEO+RRE by eliminating more redundant readers. It also kept coverage close to that of RRE, LEO and LEO+RRE.

References [1]

[2]

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[6] [7]

[8]

[9]

M. Jo, and C.-G. Lim, "RFID Tag Detection on a Water Content Using a Back-Propagation Learning Machine", KSII Transactions on Internet and Information Systems, Vol. 1, No. 1, 2007, pp. 19-32. B. Carbunar, M. K. Ramanathan, M. Koyuturk, C. Hoffmann, and A. Grama, "Redundant-Reader Elimination in RFID Systems", in Second Annual IEEE Communications and Networks (SECON), 2005, pp. 176184. Y. Bendavid, S. F. Wamba, and L. A. Lefebvre, "Proof of Concept of an RFID-Enabled Supply Chain in a B2B eCommerce Environment", in Proceedings of the 8th International Conference on Electronic Commerce (ICEC’06), 2006, pp. 564-568. S. E. Sarma, "Towards the Five-Cent Tag", Technical Report MIT-AUTOID-WH-006, MIT AUTO ID Center, 2001. D.-Y. Kim, B.-J. Jang, H.-G. Yoon, J.-S. Park, and J.-G. Yook, "Effects of Reader Interference on the RFID Interrogation Range", in Proceedings of the 37th European Microwave Conference, 2007, pp. 728-731. D. W. Engels, "The Reader Collision Problem", White Paper MIT-AUTOID-WH-007, MIT AUTO ID Center, 2001. C.-H. Hsu, Y.-M. Chen, and C.-T. Yang, "A Layered Optimization Approach for Redundant Reader Elimination in Wireless RFID Networks", in IEEE Asia-Pacific Services Computing Conference, 2007, pp. 138-145. K.-M. Yu, C. W. Yu, and Z.-Y. Lin, "A Density-Based Algorithm for Redundant Reader Elimination in a RFID Network", in Proceedings of the Second International Conference on Future Generation Communication and Networking, 2008, Vol. 1, pp. 89-92. Z.-Y. Yang and J.-L. Chen, "The Simulation and Analysis of Algorithms for Redundant Reader Elimination in RFID

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System", in Third UKSim European Symposium on Computer Modeling and Simulation, 2009, pp. 494-498. D.-H. Shih, P.-L. Sun, D. C. Yen, and S.-M. Huang, "Taxonomy and Survey of RFID Anti-Collision Protocols", Computer Communications, Vol. 29, No. 11, 2006, pp. 2150-2166. S. M. Birari, and S. Iyer, "Mitigating the Reader Collision Problem in RFID Networks with Mobile Readers", in 13th IEEE International Conference on Networks, 2005, Vol. 1, pp. 463-468. K. Finkenzeller, RFID Handbook Fundamentals and Applications in Contactless Smart Cards and Identification, Chichester, UK: John Wiley and Sons Ltd., 2003. J. Waldrop, D. W. Engles, and S. E. Sarma, "Colorwave: A Mac for RFID Reader Networks", in IEEE Wireless Communications and Networking Conference, 2003, pp. 1701-1704. J. Ho, D. W. Eagles, and S. E. Sarma, "HiQ: A Hierarchical Q-Learning Algorithm to Solve the Reader Collision Problem", in International Symposium on Applications and the Internet Workshops (SAINT), 2006, pp. 88-91. K.-I. Hwang, K.-T. Kim and D.S. Ecom, "DiCa: Distributed Tag Access with Collision-Avoidance Among Mobile RFID Readers", in EUC Workshops, 2006, pp. 413-422. J. Yu and W. Lee, "GENTLE: Reducing Reader Collision in Mobile RFID Networks", in The 4th International Conference on Mobile Ad-hoc and Sensor Networks, 2008, pp. 280-287. D. M. Dobkin, The RF in RFID Passive UHF RFID in Practice, Oxford, UK: Elsevier Inc., 2008. I. Mayordomo, R. Berenguer, and A. G. Alonso, "Design and Implementation of a Long-Range RFID Reader for Passive Transponder", IEEE Transactions on Microwave Theory and Techniques, Vol. 57, No. 5, 2009, pp. 12831290 V. D. Hunt, A. Puglia, and M. Puglia, RFID-A Guide to Radio Frequency Identification, Hoboken (N.J.), USA: John Wiley and Sons Inc., 2007 M. A. Khan, M. Sharma, and R. B. Prabhu, "A Survey of RFID Tags", International Journal of Recent Trends in Engineering, Vol. 1, No. 4, 2009, pp. 68-71.

Nazish Irfan received his B.E. degree in Electrical from GEC Raipur, India in 1992, MASc in Electrical Engineering from University of Ottawa, Canada, 2007. Presently he is a Ph.D candidate in University of Ottawa, Canada. His research interests include RFID, reader/tag anti-collision protocols, neural networks, and genetic algorithm. Mustapha C.E. Yagoub received the Dipl.-Ing. degree in Electronics and the Magister degree in Telecommunications, both from the Ecole Nationale Polytechnique, Algiers, Algeria, in 1979 and 1987 respectively, and the Ph.D. degree from the Institut National Polytechnique, Toulouse, France, in 1994. After few years working in industry as a design engineer, he joined the Institute of Electronics, Université des Sciences et de la Technologie Houari Boumédiene, Algiers, Algeria, first as an Lecturer during 1983-1991 and then as an Assistant Professor during 1994-1999. From 1996 to 1999, he has been head of the

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communication department. From 1999 to 2001, he was a visiting scholar with the Department of Electronics, Carleton University, Ottawa, ON, Canada, working on neural networks applications in microwave areas. In 2001, he joined the School of Information Technology and Engineering (SITE), University of Ottawa, Ottawa, ON, Canada, where he is currently a Professor. His research interests include RF/microwave device/system CAD, neural networks, RFID systems, , and applied electromagnetics. He has authored or coauthored over 250 publications in these topics in international journals and referred conferences. He is the first author of Conception de circuits linéaires et non linéaires micro-ondes (Cépadues, Toulouse, France, 2000), and the coauthor of Computer Manipulation and Stock Price Trend Analysis (Heilongjiang Education Press, Harbin, China, 2005). Dr. Yagoub is a senior member of the IEEE Microwave Theory and Techniques Society and a member of the Professional Engineers of Ontario, Canada.

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814

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On One Approach to Scientific CAD/CAE Software Developing Process George Sergia1, Alexander Demurov2, George Petrosyan3, Roman Jobava4 1

2

Exact and Natural Sciences department, Tbilisi State University Tbilisi, 0160, Georgia

Exact and Natural Sciences department, Tbilisi State University Tbilisi, 0160, Georgia 3

EM Consulting and Software Ltd., (EMCoS) Tbilisi, 0160, Georgia

4 EM Consulting and Software Ltd., (EMCoS) Exact and Natural Sciences department, Tbilisi State University Tbilisi, 0160, Georgia

Abstract — Development of science-intensive software products is a complex and time-consuming process that requires good management and coordinated teamwork of all members, involved in the development. Each complex software product has a number of special features; as well as each development team has some unique capabilities. In such conditions it is not always possible to apply standard methods of software engineering without adapting to the specific case. In this paper, we present a development process of largescale CAD/CAE systems for investigation of electromagnetic compatibility issues. Keywords: Software Development, Software Engineering, CAD Systems, CAE Systems, Development Process

1. Introduction For many years, software engineering has been continuously evolving and providing solutions to improve practices of software development. Many different methods and approaches have been invented to overcome difficulties, arising during software development process. Some of them have been standardized as typical software engineering techniques. All of these standards and approaches, like waterfall model[1], agile methods[2] and extreme programming[3], have shown their benefits and disadvantages when applied to specific situations. Despite the fact, that each approach offers a fairly clear advice and guidance, the practice shows

that quite often these rules cannot be applied without some modifications and adaptations to specific processes. This happens due to many factors. Among main reasons that can be identified, one of the most important is, for example, the nature of the product being developed. Development process of science-intensive programs, that perform complex calculations and simulations, differs from developing a client/server application, creating website or online store. Also, structure and members of development team have a major impact on the process, because team consists of people with different characters, skills and experience. Success of the development largely depends on how harmoniously and professionally team members cooperate. These and other factors must be considered when choosing and applying a particular approach to a specific software development process. If necessary, selected approach must be optimized and adapted according to the known recommendations, or by introducing new recommendations, based on existing experience.

2. CAD/CAE system description As new technologies appear, penetrating all spheres of human activity, need for high quality scientific software grows. Today's scientific software products are sophisticated CAD/CAE systems for simulation, visualization and analysis of real world objects and

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org processes. A lot of work is required to create such systems, together with knowledge and experience of people from different fields and with different skills. The process, described in this paper, has been established during development of several CAD/CAE systems, used for modeling and studying the problems of electromagnetic compatibility in electrical systems of modern vehicles. The software products introduced below represent the result of years of collaborative efforts of developers, academic experts in the field of electrodynamics, testers, technical writers and designers of graphical user interface. These systems are being constantly improved, updated and expanded to meet the needs of customers and target market, taking the increasing competition into account. The first complex system we will shortly describe is EMC Studio [4] - a powerful program package for the sophisticated computer analysis of electromagnetic compatibility (EMC) problems. This system allows user to simulate, conduct physical calculations and analyze the results. To handle the complexity of the numerous required tools and methods all functions and modules were integrated into one, easy to use program interface. This interface combines all tools for model generation and various methods for calculation. Fig.1 shows a screenshot of the program with an automobile model.

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equation for harmonic excitation is applied to the calculation model. 

Circuit Analysis of linear and non-linear circuits in frequency and time domain with a SPICE 3f5 compatible calculation core.



Static Analysis of linear and non-linear low frequency problems in frequency and time domain with a quasi-static approach.



Cross Talk: Analysis of linear and non-linear terminated complex cable structures in frequency and time domain. A circuit model of an arbitrary transmission line structure is generated. The included 2D field solver calculates the transmission line parameters with respect to the reference conductor. An arbitrary shaped metallic surface structure can be the reference conductor.



Radiation Hybrid: Emission analysis of linear and non-linear complex cable-antenna coupling problems in frequency and time domain. A circuit model of an arbitrary transmission line structure is automatically generated. Common mode currents on transmission lines are calculated and converted into impressed current sources on MoM segments. MoM calculates radiation and coupling to antenna.



Susceptibility Hybrid: Immunity analysis of linear and non-linear complex field-cable coupling problems in frequency and time domain. Field distribution along the cable structure is calculated. The transmission line circuit is supplemented with sources reflecting the incident field. Analysis model is calculated with a SPICE 3f5 compatible solver.

Calculation results can be viewed and analyzed in EMC Studio Post-Processing module. It is possible to view results as 3D and 2D charts:

Fig. 1. Screenshot showing model and a result of a hybrid EMC Studio calculation

For the calculation of large systems the following analysis types are necessary and are provided by EMC Studio: 

EM-Analysis of linear electromagnetic field and current coupling problems in frequency and time domain with MoM. Electrical field integral

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org

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evaluation of any harness database object or complete harnesses very easy. 3D Browser has auto-rotate functionality, which allows rotation of a model in arbitrary direction with user-defined speed. Also 3D Browser has Auto Locate function. This function brings selected object to the center of the 3D Browser area and zooms the view to provide the best fit for the model.  Fig. 2. Screenshot showing calculation results

Another CAD/CAE system for analysis of EMC problems is Harness Studio [5]. This program allows user to create, analyze and maintain a database of cable harness of vehicles.

Schematic Viewer can schematically visualize connectors or splices and their connections with other connectors or splices in database. It provides user with convenient tools for scheme rearrangement and manipulation.

Harness Studio supports four different visualizations of cable harness database – Table View, 3D View, Schematic View and Cross Section View. Extended modules for harness development and analysis, like an Expert System Option or Electrical and Thermal Analysis Option, enable fast design, rule checking or the calculation of physical properties of the harness.

Fig. 4. Harness Engine schematic viewer



Fig. 3. Screenshot showing Harness Engine structure



Table view contains detailed information about different objects in a current database. Data in table can be sorted, searched and arranged in multiple ways.



3D Browser is a powerful viewer. It supports visualization of any objects of a harness database as well as the topology. Comfortable functions for zooming, rotating, and moving make

Cross Section Viewer shows the cross-section of any segment interactively. User can click on any cable and the appropriate selection in the cable table will be shown. Colors are shown according to a customizable color list, which gives the user more realistic view of cable bundles. Cable objects are linked to the database. It provides convenient way for distance measurement between arbitrary points in cross section area.

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org

Fig. 5. Harness Engine schematic viewer

Expert System module extends Harness Studio to powerful rule checker with full functionality, developed on the basis of Expert System language CLIPS. Complex cable harnesses can be checked for compliance with arbitrary electrical, mechanical and economical rules. The use of Expert System functionality is very comfortable due to the sophisticated pre- and post-processing functions. Similar to the natural language, IF-THEN rules can be formulated and applied to cable harness systems. As it can be seen from the description of complex software systems, many different specialists are involved in the development process. Correct and optimal organization of the process is crucial for achieving success.

3. Software Engineering Process Any software development process begins with specification of basic requirements for the final product. When it comes to upgrading of an existing version, then new requirements must be formulated, which define a changed project scope. For software products described in the previous section, we deal with this particular case. These products are being developed for several years and constantly require innovations and new functionality. The introduction of new features and technologies does not always go smoothly. This often leads to the need for significant changes in software architecture to meet the new requirements and conditions. Requirements are formulated based on market analysis and requests from customers, using earlier versions of the product. Since the described systems are positioned as commercial products, targeting certain categories of customers, the goals and development objectives are mainly generated by marketing group, which monitors and analyzes the current market trends, determines which requirements must be implemented, and what can be postponed to the future releases.

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Based on the specified requirements, the list of tasks is compiled, which basically involves changes and additions to the existing functionality, improved user interface and other required features. The list of tasks is then extended to the basic implementation plan. When creating a general plan, sometimes it is difficult to determine what amount of work has to be performed during each task, so entire development period is divided to several main stages, each ending with a specific milestone [6]. Within these stages, a preliminary assessment of work is performed and more realistic schedule is determined. Milestones provide an opportunity to get a limited, but working version of the product at some stage during development. At the same time Milestones help to evaluate the performed activities and identify general problems of the development process. The number of milestones should not exceed 4, including the phase of beta testing of the product. The duration of each phase should be limited to 1.5 - 2 months. This is a flexible approach for easily switching from development version to beta testing stage, when for some reasons, the product must be released earlier, or it is necessary to correct task lists in case of a risk of falling behind the schedule. During development, resources for tasks are allocated according to their priority and complexity. Higher priority and time-consuming tasks must be performed at the early stages and should be included in the first and second milestone versions. Priority should be given to the tasks, which introduce new important functionality, because such approach provides additional competitive advantages to the product. It should be noted that the priority might also be affected by the complexity of the task. Since the complex systems consist of a set of subsystems, or modules, often changes may affect multiple subsystems. Sometimes improvements and optimizations may be considered for modules that already have passed an extensive testing. As a result, the task becomes complicated and requires a careful approach for further development. In order to maintain the overall quality of the product, number of scheduled timeconsuming tasks should be kept to a minimum as development process reaches milestone of beta-version preparation. Practice has shown that when working on a specific task, if the estimated time required for the task exceeds 3-4 weeks, better results can be achieved by breaking it into several subtasks. As it was already mentioned, a subtask may require development, or changes in the different software modules of the system. Different modules within a single task developed and managed by independent groups, often lead to the loss of overall control, failure to meet the scheduled deadlines, reduction of product quality, etc. The main reason for this is the problem of communication between

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org groups that may occur due to objective reasons. Past experience, gained during development of the mentioned software products shows us that when working plans of each independent group are poorly coordinated, the development process slows down. Since these products are being developed during several years and many different specialists were being involved in the process at different stages, situations occurred when there has been a conflict between software modules due to the inconsistent approaches or development technologies. Problems may also arise in case, when the requirements contain specific scientific information. Usually, when such requirements are considered, scientific experts can initiate theoretical researches in order to investigate tasks in depth. After researches, a prototype of calculation module is created and verified for reliability. Information about research results and overall task must be clearly formulated to all groups involved in the implementation of the subtask. Therefore, if the work is carried out in parallel, there is a need for scientific experts to participate in all working groups at the same time, which may be physically impossible. Human factor is also very important in software development process. Conflict of interests between the group leaders due to technical or organizational issues may seriously harm the development process. Naturally, increasing the time for discussions and agreements on various aspects of development can minimize such problems, but frequent discussions will inevitably arise the idea of bringing together the necessary human resources under the single management mechanism and uniting the all these works as a one whole. Our practice has shown that separate project team should be created for each subtask (project). As mentioned above, building a complex scientific system needs resources, knowledge and experience of people of different professions. Typical human resources that are needed for such projects are: project manager, graphical interface developers, computational methods developers, technical writer, graphics designer, tester, customer (or product manager), and science field experts.

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Fig. 5. Available human resources

The project team is created from the available human resources (Fig. 5). Specialists in different areas are involved in the process, depending on the main goals. During team creation a situation may arise, when the necessary human resources are members of other projects teams. In this case, based on the priority of the task the decision should be made to postpone the new task until all the necessary resources are available, or launch a new project, pausing the current project(s). Such decision should be made as a result of agreement between all responsible persons involved in the current and upcoming projects. This means that the working team is not a constant group of developers, but is created "on-the-fly" for each specific project. For example, lets consider following requirements: it was decided to create new calculation solver to introduce new optimized methodic for calculation and analysis of specific electromagnetic problems. New solver must be included in system, therefore GUI support for this solver must be provided. This means that several changes must be made in system interface: user must be able to set up the task and run calculation with different parameters, using this solver and system must give possibility to analyze results of calculation in 2D/3D post-processing of software. Based on this description, we can decide who must be included in project group in order to fulfill task. These must be: developer of solver and field expert (sometimes is the same person), developers of graphical user interface (2-3 persons, because changes are separated on several modules – calculation module, post processing module etc.), technical writers (describe new solvers and changes in GUI), testers (no more than 1-2 persons – testing and benchmarking of solver and testing of GUI can be done separately at the beginning of project, but overall testing must be conducted at the end). Accept mentioned persons, project manager and product manager are included in the group as well. So, we have got a project group consisting of maximum 8-10 persons. Here we considered complicated task, but usually tasks are not so complicated and in one project group there can be up to 5-7 persons at average. Given the above recommendations, described problems can be eliminated within a project team. Estimated total amount of work, the execution flow and the role of each participant of the project team can be clearly stated and agreed. A single person - project manager - is responsible for the project during development process.

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recommendations have formulated based on experience that has been gained in a specific environment. Using mentioned rules and recommendations in practice, helped us to improve overall development process thus making it more flexible and well controlled. Despite the fact that described approach does not apply for universality, formulated ideas can become the basis of more general recommendations for development processes of similar complex software products There are some questions, not mentioned in this paper, which should be optimized and improved, so researches in that direction will be continued. Fig. 6. Project group creation

Each project manager can choose any approaches of development from the wide range of existing methods, including extreme programming model, because one of the main conditions of this approach is met - a customer (product manager) is a member of the project team. For the described products project managers have been using iterative development techniques. Based on our experience, the average period of iterations should not exceed one week, or 10 days at the most. Iteration includes planning, development and testing. The main planning works are done during the first iteration, and are subject to revision at the beginning of each subsequent iteration. Testing can begin by the end of the first iteration and must be performed continuously until the completion of the project. For improving control and eliminating misunderstanding, the development process must be well documented. The documents usually include proposal, software requirements specification, GUI design presentation, architectural design, technical documentation, overall description of the project and test report. Each project team member is responsible to prepare documentation concerning its part of process, but project manager is the person who accumulates all documents, archives them and passes to all interested departments (for example, to quality assurance department, or support department). When stage of beta release arrives, the intensity of product's quality control must be increased, at the same time reducing the intensity of the development. After beta version release, all processes related to implementation of new features must be finished.

4. Conclusions Present paper describes some recommendations for development of complex CAD/CAE systems. These

References [1] Winston W. Royce, "Managing the Development of Large Software Systems", Proceedings of IEEE WESCON, 1970 [2] Alistair Cockburn, “Agile Software Development”, Addison-Wesley, 2001 [3] Kent Beck, “Extreme Programming Explained: Embrace Change”, Addison-Wesley, 1999.

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org [4] [5] [6]

EMC Studio Manual, Version 5.0, EMCoS Ltd., http://www.emcos.com Harness Studio Manual, Version 4.0, EMCoS Ltd., http://www.emcos.com Ed Sullivan, “Under Pressure and on Time”. Microsoft Pr., 2001

George Sergia: PhD research scholar at the Exact and Natural Sciences department, Tbilisi State University. Area of research is software engineering and development of complex scientific CAD/CAE systems. Has a Master degree in Computer Science from Tbilisi State University. Awarded with “Best Scientific paper” by Georgian National Scientific Fund for Paper, presented on WSEAS European Computing Conference (ECC’09). Currently works as a project manager in one of the leading EM software development companies, EMCoS (www.emcos.com), Georgia Alexander Demurov: PhD research scholar at the Exact and Natural Sciences department, Tbilisi State University. Area of research is development of complex CAD systems for simulation of PCB problems. Has a Master degree in Computer Science from Tbilisi State University. Currently works as a product manager in one of the leading EM software development companies, EMCoS (www.emcos.com), Georgia George Petrosyan: Has received his Master degree at the Exact and Natural Sciences department, Tbilisi State University in 2010. Area of research is development of expert system for analysis of automotive cable harnesses. Currently works as a project manager in one of the leading EM software development companies, EMCoS (www.emcos.com), Georgia Roman Jobava: (M’97) was born in Georgia, in 1965. He received the M.S. and Ph.D. degrees in Radiophysics from I. Javakhishvili Tbilisi State University, Tbilisi, Georgia, in 1987 and 1990, respectively. Since 1996 he has been working at I.Javakhishvili Tbilisi State University as Associate Professor. From 1987 to 2000 he was a Senior Researcher in Laboratory of Applied Electrodynamics of the same University, responsible for computer simulation of transient electromagnetic phenomena and development of numerical and software tools. In 2001 he began leading EM Consulting and Software, EMCoS Ltd. His research interests include numerical methods in applied electrodynamics, computer simulation of EMC problems, ESD, and transient field calculations, software development for solving EM/EMC problems.

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IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814

Modeling Throughput Performance in 802.11 WLAN Moses Ekpenyong1 and Joseph Isabona2 1

2

Department of Computer Science, University of Uyo Uyo, Akwa Ibom State 520001, Nigeria

Department of Basic Sciences, Benson Idahosa University Benin City, Benin, Nigeria

Abstract This paper presents a mathematical framework for maximizing throughput in Wireless Local Area Network (WLAN) channels using key system design parameters such as packet length and transmission rate. We study the tradeoff between the throughput and choice of some design variables through extensive computer simulation. We observe from the simulation results that the design parameters are highly signal dependent and can dynamically be adapted to improve the overall system performance, principally in the area of data transmission and reception in WLAN channels. Keywords: Throughput optimization, WLAN system, payload traffic, AWGN channels.

1. Introduction Wireless Local Area Networks have become common place network technology. Recent advances in wireless technologies provide portable devices with wireless capabilities that allow network communication for both mobile and non-mobile users. Wireless technologies have several key benefits that can be applied to new and existing networks. Some of these benefits include flexibility, integrity and reliability, reduced cost, scalability and adaptability. Just like wired LANs, which use twisted or fibre-optic cables as transmission medium, WLANs also transmit through a medium. WLAN employ either infrared (IR) light or radio frequency (RF). Of these two media, RF is far more popular for its longer range, higher bandwidth and wider coverage. Most WLANs today use the 2.4GHz frequency band, the only portion of the RF spectrum reserved around the world for unlicensed devices. The freedom and flexibility of wireless networking can be applied both within and between buildings and can be used to replace or augment an existing wired network. As with wired LAN systems; the actual throughput in WLAN is product and set-up dependent. The factors

affecting throughput include the number of users, propagation factors such as distance and multipath, the type of WLAN system employed, as well as the latency and bottlenecks on wireless portions of the LAN. Throughput is defined as the ratio of the expected delivered data payload to the expected transmission time. It is the percentage of undistorted data packets received without errors. Moreover, most voice and video applications prefer very small payload sizes to ensure reliable and minimum delay service delivery. Throughput is what the user sees after overheads. Overheads consume part of the nominal bit rate including: (i) lost air time from collisions between access points (APs) and stations (STAs) trying to transmit simultaneously. (ii) Idle guard times, built into the protocol. (iii) Airtime absorbed by beacon frames. (iv) Protocol overheads (e.g. synchronization, address, acknowledgement and control). (v) Switching to ensure compatibility. In this paper, we study the WLAN data transmission configurations, propose and simulate a reliable data transmission model for efficient network deployment.

2. Statement of the Problem Throughput is affected by several factors. It is the percentage of undistorted packets the user sees after overheads. The resulting impact on throughput efficiency depends on the traffic mix. We highlight below the problems affecting WLAN throughput: (i) Nominal bit rate downshifts with distance As the distance increases, the radio signal gets weaker and more distorted. As the signal degrades further, a “downshift” to a lower bit rate occurs. Downshifting allows the radio link to use simpler modulation scheme that makes it easier

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IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org for the equipment to distinguish between digital zeroes and ones. (ii) Contention between multiple-active users The throughput decreases if there are multiple simultaneously-active users transmitting data on the WLAN radio channel. The reason for this decrease is that these users experience collision and the colliding parties must wait for a defined “backoff” period before retransmitting. This results in lost airtime which affects the system’s throughput. (iii) Interference and coexistence The unlicensed nature of radio-based WLANs means that other products that transmit energy in the same frequency spectrum can potentially provide some measure of interference to a WLAN system. Microwave ovens are of potential concern, but most WLAN manufacturers design their products to account for this interference. Interference is a growing headache as WLAN popularity grows. It mostly arises from other APs and STAs on the same and/or adjacent radio channels, and can be mutual and harmful. To minimize interference, different radio channels can be used. Frequency optimization products are emerging to help manage interference. Another concern is the co-location of multiple WLAN. While WLANs from some manufacturers interfere, other coexists without interference. This issue is best addressed with the appropriate vendors. (iv) Interoperability of wireless devices Wireless LAN systems from different vendors may not be interoperable for the following reasons: First, different technologies will not interoperate. For instance, a system based on spread spectrum frequency hopping technology will not communicate with another system based on spread spectrum direct sequence technology. Secondly, systems using direct frequency bands will not interoperate even if they employ same technology. Thirdly, systems from different vendors may not interoperate even if they both employ same technology. Due to increasing users’ capacity and variety of wireless applications, telecommunication service providers need to provide better quality of service (QoS) to the end users. In this paper, we investigate how to provide improved system’s throughput and guarantee quality services in WLAN communication, taking into account the impact of the channel conditions.

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3. Review of Related Works Several researches [3-4, 7] have studied the efficiency of the IEEE 802.11 protocol by investigating the maximum achievable throughput under various network configurations. They analyze the backoff mechanism and propose alternatives to improve the performance of existing standard mechanisms. In [3], a simple analytical model that computes saturation throughput performance assuming finite number of stations and ideal channel conditions is presented. This model extended in [4], with a consideration of the frame retry limits, which precisely predicts 802.11 DCF throughput. However, no known work yet exist that considers finite load throughput of 802.11 DCF and that which considers protocol parameters such as timeouts. A number of studies have emerged on the performance of WLANs using the 802.11 MAC protocol. Some preliminary investigations on the voice capacity of the IEEE 802.11b network have been conducted in [1], where they observed that in good channel conditions, a higher number of voice users can be supported by using larger payload sizes. The use of theoretical framework for rate adaptation to evaluate IEEE 802.11 is investigated in [2]. The authors propose a computationally expensive dynamic programming approach to find the optional data rates that can be used for fairly simple wireless channel variation models with known probability of transitions from good to bad states and vice versa. They accomplished this by using a discrete Markov Chain. A link adaptation strategy for IEEE 802.11b is provided in [5], where the frame lengths are classified into three broad categories: 0 - 100 bytes, 100 - 1000 bytes and 1000 - 2400 bytes. An early investigation on the effect of payload size on throughput is conducted in [6]. This paper considers the payload length as an optimization parameter and proposes a cross-layer scheme that jointly optimizes users’ throughput in IEEE 802.11a WLAN based on channel conditions. The theoretical formulation in this paper allows payload to be varied continuously over a wider range. A mathematical framework is then formulated to dynamically adapt the payload length to maximize the throughput for Additive White Gaussian Noise (AWGN) under different fading channels.

4. Throughput System Modeling We have defined throughput as the number of payload bits per second received correctly. For initial analysis, we consider the effect of payload variation in AWGN

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org channels and assume that the acknowledgements from the receiver are free of errors. The throughput corresponding to the physical layer (PHY) mode j is given by: L (1) T ( j)  bR P j  , b, L  LCj

j

s

s

where L is the payload length in bits C j is the header and DCF overhead corresponding to rate ‘j’ in bits. R j is the data rate corresponding to PHY mode j. b is the number of bits per MQAM symbol j

Ps is the packet success rate (PSR) defined as the probability of receiving a packet correctly corresponding to PHY mode j.  s is the SNR per symbol and is given by

P  s  s N  o N o Rs

(2)

where  s , N o , and P represents the energy per symbol, the one-sided noise power spectral density and the received power respectively. C j takes into account the CSMA/CA channel access time and the header overhead as specified by the IEEE 802.11 protocol. The time delay is converted to bytes for the purpose of optimization by the expression C j  Rs  Tovh (3) where Rs is the transmission rate corresponding to PHY mode ‘s’ and Tovh is the total protocol overhead. Since any symbol error in the packet results in a loss of the packet, the packet success rate (PSR) is given in terms of the symbol error rate Pe by

Ps  s , b, L   1  Pe L, b,  s  (4) where the Pe of MQAM in AWGN channels for the j

various PHY models in IEEE 802.11a is derived in [2]. 

In order to find the optimal payload length, L , we assume that the payload length L continuously varies. Differentiating equation (1) with respect to L and applying equation (4) produces the derivative in equation (5). T( j) L R 1 P j  L ln 1 P j    R Cj 1 P  L (5) L

j

L  Cj

e

s

e

j

L2

e

s

Setting this derivative to zero, we obtain a quadratic equation in L with the root. Cj 1 4bC j (6)  L   C 2j  2 2 ln 1  Pe  s , b 

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Thus, the optimal packet length L depends on the SNR per symbol  s and the probability of the symbol error Pe . To find the data rate R j that maximizes the throughput, we differentiate equation (1) with respect to R j and obtain the following condition

P  , L   T ( j ) L L  Ps  , L   Rj s R j L Cj  s R j L Cj

(7)

P  , L    Pj T ( j ) L  Ps  , L   R j s R j  s  N o R j L Cj

   

(8)

Next, we set this derivative in equation (8) to zero  Pj   P , L      0 Ps  , L     N R    s   o j 

(9)

Hence

Ps  , L    s

f  , L   s

(10)

We adopt the notation    for signal-to-interference and noise ratio (SNR) that satisfies equation (10). Combining equation (10) and (4), we arrive at an equation 

for obtaining the preferred SNR per symbol



Pe b,  s  1  Pe b,    L  s s s b

 s

 s

 s



thus, (11)

where Pe of MQAM in AWGN channels [7] is approximately given by:

  3 Pe b,  s*  4 1  2 b / 2 Q b  s   2 1  where Q is the error function and is given by



 

Q( x)  Once



 s

1 2



e (t

2

(12)

x) / 2

is determined, the optimal symbol rate is

obtained from equation (2) as

Rs 

P  No

(13)

* s

Note that the solution

 s

in equation (11) and (12)

depends on only design parameters b and L, but is independent of the received power level. Using equations (2) – (12), equation (1) can be rewritten as    3 LC T j   bRs 1  41  2 b / 2 Q b  s  (14) L  2  1   Equation (14) is computed under non-fading or slowly fading channels where the fade duration is longer than a packet period. Considering a flat Rayleigh fading

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org 

channel, the average probability of the symbol error Pe approximates to [8]:     3 s   Pe  b,  s   2 1  2 b / 2 1      2 2b  1  3 s 









    

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The input parameters were used to simulate the derived model in section 4. The following section analyses and interprets the results obtained from the simulation.

(15)

Also, rewriting equation (1) from equation (15) gives T j 

  LC  bRs 2 1  2 b / 2  L  





     3  1   s      2 2 b  1  3  s       

(16)

5. Simulation Input and Form Design Table 1 shows the parameter list and values for the simulation input. The table represents empirical parameters obtained from ideal conditions. Our reason for simulating with ideal environmental conditions is to make the simulation real and suitable for precise prediction.

Figure 2. Input form for Symbol error rate simulation

Table 1. Model input parameters Parameter Value Packet Length (L) 64, 128, 256 Data rate (Rs) 500, 250, 150 Date transmission overhead 20

Tovh 

Number of bits (b)

Bit error rate Pe  s 

1, 2, 4, 6, 8 0.01 – 0.1

SNR

(1 – 10)dB

 s 

Input forms were designed to accept the various parameters for the simulation. Figures 1- 3 show the sample input forms for the simulation:

Figure 3. Input form for simulation interface

6. Discussion and Interpretation of Simulation Results In this section, we represent the simulation results graphically to enable us interpret them with precision.

Figure 1. Input form for Optimum packet length simulation

In Figure 4, the optimum packet length (L*) is a decreasing function of the bit error rate (BER). As the bit error rate decreases, the packet error rate approaches zero, and the optimum packet length increases without bounds. In Figure 5, since BER decreases with increased SNR, the optimum packet length increases monolithically with the SNR. This shows that better channels support longer packets. Of course, in practice, the packet length (L) is an

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org integer or the smallest integer greater than the optimum packet length (L*).

Bit error rate

1.20

Figures 6 and 7, shows the throughput as a function of the BER, with varied number of bits. We observe from these graphs that as the BER increases, the rate necessary to maximize the throughput increases. This performance indicates a strong relationship between the throughput and BER and calls for a good choice of appropriate parameters for effective WLAN data transmission and reception.

1.00

0.80

0.60

In figures 8 and 9, graphs of throughput vs. SNR are presented, with varied transmission rate. As observed from these graphs, the throughput increases with SNR, such that the higher the transmission rate, the higher the throughput. This shows that throughput can be jointly optimized with the SNR and transmission rate.

0.40

0.20

450.00 Optimumpacket length (L)

20

0.00 0

2

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12 SNR

400.00

Figure 5. A graph of bit error rate vs. SNR per symbol in Rayleigh fading

Data rate = 500 Data rate = 250 Data rate = 150

350.00

300.00

Throughput

800.00

250.00

700.00

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600.00 150.00

500.00

b=2 b=4

100.00

400.00

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Bit error rate

Figure 4. A graph of optimum packet length vs. bit error rate

200.00

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12 SNR

Figure 6. A graph of Throughput vs. SNR per symbol in Rayleigh fading, with varied number of bits

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org 400.00 Throughput

1.20E+04 Throughput

21

350.00

1.00E+04 300.00

8.00E+03 250.00

b=2 b=4

6.00E+03

Data rate = 500 Data rate = 250 Data rate = 150

200.00

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12 SNR

SNR

Figure 8. A graph of bit error rate vs. SNR per symbol in Rayleigh fading with varied data rate 6.00E+03 Throughput

Figure 7. A graph of Throughput vs. SNR per symbol in non-Rayleigh fading, with varied number of bits

5.00E+03

4.00E+03

Data rate = 500 Data rate = 250 Data rate = 150

3.00E+03

2.00E+03

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Figure 9. A graph of bit error rate vs. SNR per symbol in non-Rayleigh fading with varied data rate

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org

5. Conclusions This paper has presented a framework for achieving higher throughput by careful adaptation of packet (payload) length and data rate with varying channel conditions. We studied the WLAN data configurations, and have proposed a reliable data transmission model for efficient network deployment. A mathematical model under fading and non fading channels that improves the system’s throughput for optimal performance in generic WLAN systems is also presented. The proposed model has been evaluated through extensive computer simulation subject to both Rayleigh and Non-Rayleigh fading channels. The simulation results were analyzed and relationships between the model parameters established.

References [1]

[2]

[3]

[4]

[5]

[6]

[7] [8]

D. P. Hole and F. A. Tobagi. “Capacity of an IEEE 802.11b Wireless LANS Supporting VOIP”, in Proceedings of IEEE ICC, 2004, pp. 196 – 201. D. Qiao, S. Choi and K. G. Shin. “Goodput Analysis and Link Adaptation for IEEE 802.11a Wireless LANs”. IEEE Transactions on Mobile Computing. Vol. 1. No. 4, pp. 278-292. G. Bianchi. “Performance Analysis of the IEEE 802.11 Distributed Coordination Function”. IEEE Journal on Selected Areas in Communications. Vol. No. 3, 2000, pp. 535-547. H. Wu, Y. Peng, K. Long, J. Cheng and J. Ma. “Performance of Reliable Transport Protocol over IEEE 802.11 Wireless LAN: Analysis and Enhancement”, in Proceedings of IEEE INFOCOMM, Vol. 2, pp. 599-607. J. P. Pavon and S. Choi. “Link Adaptation Strategy for IEEE 802.11 WLAN via Received Signal Strength Measurement”, in Proceedings of IEEE ICC, 2003, pp. 1108-1113. P. Lettieri and M. B. Srivastava. “Adaptive Frame Length Control for Improving Wireless Link Throughput, Range and Engery Efficiency”, in INFOCOM, Vol. 2, 1998, pp. 564-571. J. G. Proakis. Digital Communications. New York, NY, McGraw-Hill Inc., third ed., 1995. A. Goldsmith and S. G. Chua, “Variable-rate variable-power M-QAM for fading channels,” IEEE Transaction on Communication., vol. 45, pp. 1218–1230.

Ekpenyong, Moses. Moses Ekpenyong is a lecturer the Department of Mathematics, Statistics and Computer Science, University of Uyo, Nigeria. He obtained his B.Sc. and M.Sc. degrees in Computer Science, but his PhD (in view) is in the area of Speech Technology. He is a member of the following professional bodies: Nigerian Association of Mathematical Physics

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(NAMP), Nigeria Computer Society (NCS), International Speech and Communications Association (ISCA), Nigeria Mathematical Society (NMS), Nigerian Statistical Association (NSA) and WestAfrican Linguistics Society (WALS). He is an International researcher and enjoys fruitful collaboration with academicians both within Nigeria and abroad (from other fields) and has published widely in the area of wireless communications. His area of specialization is speech modeling and communications technology. Isabona, Joseph is a Lecturer at the Benson Idahosa University, Benin City, Nigeria. He has B.Sc. and M.Sc. degrees in Theoretical Physics and Physics Electronics/ Communications respectively. His PhD (in view) is in the area of Physics Electronics. He is a member of the Nigerian Association of Mathematical Physics (NAMP). He has published both nationally and internationally in the area of wireless communications. His area of specialization is signal processing and radio resource management in wireless networks.

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814

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Hello Flood Attack and its Countermeasures in Wireless Sensor Networks Virendra Pal Singh1, Sweta Jain2 and Jyoti Singhai3 1

Department of Computer Science and Engineering, MANIT Bhopal, M.P., India

2

Department of Computer Science and Engineering, MANIT Bhopal, M.P., India

3

Department of Electronic and Telecommunication, MANIT Bhopal, M.P., India

Abstract Wireless sensor network have emerged as an important application of the ad-hoc networks paradigm, such as for monitoring physical environment. These sensor networks have limitations of system resources like battery power, communication range and processing capability. Low processing power and wireless connectivity make such networks vulnerable to various types of network attacks. One of them is hello flood attack, in which an adversary, which is not a legal node in the network, can flood hello request to any legitimate node and break the security of WSN. The current solutions for these types of attacks are mainly cryptographic, which suffer from heavy computational complexity. Hence they are less suitable for wireless sensor networks. In this paper a method based on signal strength has been proposed to detect and prevent hello flood attack. Nodes have been classified as friend and stranger based on the signal strength. Short client puzzles that require less computational power and battery power have been used to check the validity of suspicious nodes.

Keywords: WSN, client puzzles, signal strength.

1. Introduction Wireless sensor networks are a particular type of ad hoc network, in which the nodes are ‘smart sensors’. Sensors are small devices equipped with advanced sensing functionalities (for monitoring temperature, pressure, acoustics etc.), a small processor, and a short-range wireless transceiver [1]. In this type of network, the sensors exchange information about the environment in order to build a global view of the monitored region. This information is made accessible to the external user through one or more gateway nodes. Sensor networks are expected to bring a breakthrough in the way natural phenomena are

observed: the accuracy of the observation will be considerably improved, leading to a better understanding and forecasting of such phenomena. WSN technology enables monitoring of vast and remote geographical region, in such a way that abnormal events can be quickly detected. The cost of sensor nodes varies from hundreds of dollars to a few cents, depending upon their size and complexity. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and transmission range. [1]

2. Attacks on Sensor Networks Most sensor network routing protocols are quite simple, and for this reason are sometimes even more susceptible to network attacks as compared to general ad-hoc routing protocols. Most network layer attacks against sensor networks fall into one of the following categories: [2]

2.1 Spoofed, altered, or replayed routing information One direct attack against a routing protocol is to target the routing information exchanged between nodes by spoofing, altering, or replaying routing information. Adversaries may be able to create routing loops, attract or repel network traffic, extend or shorten source routes, generate false error messages, partition the network, increase endto-end latency by using this type of attack. [2]

2.2 Selective forwarding

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org In selective forwarding attack, malicious nodes may refuse to forward certain messages and simply drop them, ensuring that they are not propagated any more. A simple form of this attack is: when a malicious node behaves like a black hole and refuses to forward every packet it receives. However, such an attacker runs the risk that neighboring nodes will conclude that this node has failed and decides to seek another route. A more subtle form of this attack is when an adversary selectively forwards packets. An adversary interested in suppressing or modifying packets originating from few selected nodes can reliably forward the remaining traffic and limit suspicion of its wrongdoing. [2]

2.3 Sinkhole attacks In a sinkhole attack, the attacker’s goal is to lure nearly all the traffic from a particular area through a compromised node, creating a sinkhole with the adversary at the centre like black hole attack in ad hoc networks. Sinkhole attacks typically work by making a compromised node look attractive to surrounding nodes with respect to the routing algorithm. [2]

2.4 The Sybil attack In Sybil attack, a single node presents multiple identities to other nodes in the network. The Sybil attack can significantly reduce the effectiveness of fault-tolerant schemes such as distributed storage, multipath routing, and topology maintenance. Replicas, storage partitions and routes believed to be used by disjoint nodes could in actuality be used by one single adversary presenting multiple identities. [2]

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2.6 Hello flood attack Some routing protocols in WSN require nodes to broadcast hello messages to announce themselves to their neighbors. A node which receives such a message may assume that it is within a radio range of the sender. However in some cases this assumption may be false; sometimes a laptop-class attacker broadcasting routing or other information with large enough transmission power could convince every other node in the network that the attacker is its neighbor. For example, an adversary advertising a very high quality route to the base station could cause a large number of nodes in the network to attempt to use this route. But those nodes which are sufficiently far away from the adversary would be sending the packets into oblivion. Hence the network is left in a state of confusion. Protocols which depend on localized information exchange between neighboring nodes for topology maintenance or flow control are mainly affected by this type of attack. [3] An attacker does not necessarily need to construct legitimate traffic in order to use the hello flood attack. It can simply re-broadcast overhead packets with enough power to be received by every other node in the network. [3]

2.5 Wormholes In the wormhole attack, an attacker tunnels messages received in one part of the network over a low latency link and replays them in a different part of the network. The wormhole puts the attacker nodes in a very powerful position compared to other nodes in the network. For instance in reactive routing protocols such as AODV or DSR, the attackers can tunnel each route request RREQ packet to another attacker which near to destination node of the RREQ. When the neighbors of the destination hear this RREQ, they will rebroadcast this RREQ and then discard all other received RREQs in the same route discovery process. [2]

Figure 1(a) shows an attacker broadcasting hello packets with more transmission power than a base station. Figure 1(b) shows that a legitimate node considers attacker as its neighbor and also as an initiator. [3]

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org

3. Countermeasures against Hello Flood Attack Multi-path multi-base station data forwarding technique is proposed in [3], in which a sensor node maintains number of different secrets (keys) in a multiple tree. Sensor node can forward its sensed data to multiple routes by using these secrets. There are multiple base stations in the network that have control over specific number of nodes and also, there are common means of communication among base stations. Each base station has all the secrets that are shared by all the sensor nodes, covered by it, according to the key assignment protocol. Given the shared secret and the generated new key between two sensor nodes, the process of route setup requires much processing hence is inefficient. In [4] author suggests that hello flood attack can be counteracted by using “identity verification protocol”. This protocol verifies the bi-directionality of a link with encrypted echo-back mechanism, before taking meaningful action based on a message received over that link. This defense mechanism becomes in effective when an attacker has a highly sensitive receiver and a powerful transmitter. If an attacker compromises a node before the feedback message, it can block all its downstream nodes by simply dropping feedback messages. Thus, such an attacker can easily create a wormhole to every node within range. Since the links between these nodes and attacker are bidirectional, the above approach will unlikely be able to locally detect or prevent a “hello flood”. Considering the scarcity of energy resources of sensor nodes, the authors have proposed in [5] a probabilistic based approach, which forces few randomly selected nodes to report to base station about hello requests. The base station then further analyzes the request authenticity. In [2] a cryptographic technique is used to prevent the hello flood attack. Any two sensors share the same secret key. Every new encryption key is generated on fly during the communication. This phenomenon ensures that only reachable nodes can decrypt and verify the message and hence prevent the adversary from attacking the network. But the main drawback of this approach is that any attacker can spoof its identity and then generate attacks. In [6] the authors have proposed a security solution framework tailored to the base station for defending against DoS attack. After initial DoS detection, base station challenges clients with cryptographic puzzles to protect itself from different types of attacks. Compared with traditional puzzle schemes, they introduce a novel reputation based client puzzles, which applies a dynamic

25

policy to adjust the puzzle difficulty for each node in terms of node’s reputation value. Hence the punishment for malicious nodes becomes more and more pressing without introducing extra unnecessary burden to most normal nodes. A security mechanism based on signal strength and geographical information is proposed in [7] for detecting malicious nodes that launching hello flood and wormhole attack. The idea is to compare the signal strength of a reception with its expected value, calculated using geographical information and the pre-defined transceiver specification. The detection rate of the solution depends on different parameters such as network density, transmission power multiplier of the malicious node, message checking probability etc. In [8] a compromised network scenario, when the adversary with sensitive receiver, broadcasts a request like Hello with noticeable power, many nodes hear it at the same time, the nodes try to reply using two way or more way handshake protocol, to this message in order to announce their presence. However the healthy nodes have small transmission and carrier sense ranges. So those located farther than the carrier sense range of each other will try to send the messages back simultaneously. The core idea is to tune the channel access and transmission parameters so that the responses of these nodes collide with each other due to the high density in arrival time and prevent the adversary from decoding the messages correctly. This way the adversary will not be able to hear the victims’ replies and is obliged to reduce his power and act just like a normal node in the ideal form. This is like a well-known hidden node effect in wireless ad hoc networks. In fig. 2, node “A” represents the attacker with high transmission range equipped with sensitive receiver while “B”, “C” and “D” stand for healthy nodes whose carrier sense ranges are shown by dark circles around them. “b”, is a healthy node whose transmission is blocked and backed off due to the transmission of other nodes[8].

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org Figure (2)

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receiving node if the received signal power Pr is equal or greater than the received signal power threshold Pthres.

A threshold based solution is proposed in [9] to defend against flooding attacks in MANET. The mobile nodes use a threshold value to check whether its neighbors are intruders or not. When the number of route request packets broadcasted by a node exceeds the predefined threshold value, it is treated as an intruder and the node stops providing its services to the intruder.

When any laptop class attacker sends hello message to a legitimate node in a fixed radio range then the receiving node checks its hello message signal strength, if it is same then requesting node is a legal node of the network; if it differs, it categorizes the sender node as stranger.

4. Proposed Scheme

Signal strength = Fixed signal strength in radio range=friend Signal strength > Fixed signal strength in radio range=stranger

In this paper we have proposed a solution for detection of hello flood attack which is based on signal strength and client puzzles method. Signal strength of all sensor nodes is assumed to be same in a radio range. Each node checks the signal strength of the received hello messages with respect to known radio range strength; if they are same then sender node is classified as a “friend” else sender is classified as a “stranger”. When any node is classified as a stranger, we try to check its validity using some client puzzles. Dynamic policy technique is used to adjust the difficulty of puzzle level for each node in terms of number of hello messages sent. The more the number of hello message sent by a node, more will be the difficulty of the puzzles it has to solve. Some primary assumption are(1) Communication is within fixed radio range. (2) All sensor nodes in a fixed radio range have same transmitting and receiving signal strength. (3) All sensor nodes are homogeneous (same hardware and software, battery power etc.). (4) Every sensor node knows the fixed signal strength used in its communication range. (5) A time threshold is used, which denotes the expected time of reply message. (6) A hello message counter has been used by all sensors to keep the record of number of hello requests received in an allotted time. Initially signal strength is calculated as two ray propagation model [10] Pr= (Pt*Gt*Gr*Ht2*Hr2)/(d4*L)

(1)

In eq. 1 Pr is received signal power (in watts), Pt is transmission power (in watts), Gt is the transmission antenna gain , Gr is the receiver antenna gain, Ht is the transmitter antenna height(in meter) and Hr is the receiving antenna height(in mete), d is the distance between transmitter and receiver (in meter), and L is the system loss(a constant). A signal is only detected by a

If signal strength of received hello message is approximately same but not equal to fixed signal strength then it may be a stranger or a friend. To distinguish between a friend and a stranger we apply a technique based on client puzzles. The puzzles used take less memory and computation power. The node sends some puzzles to the requesting node; if the correct reply comes in allotted time threshold then the node is considered as a friend, if not then it is treated as stranger.

4.1 Algorithm for hello flood prevention Begin INPUT: Signal Strength 1: If a node receives hello message from a node S then 2: if Signal strength of received hello message = fixed signal strength in radio range 3: then node s is classified as a friend 4: Node accepts hello message and perform necessary function 5: Else 6: if Signal strength of received hello message ≈ fixed signal strength in radio range 7: then nodes sends puzzle to node S 8: If reply message of correct answers comes in fixed time threshold 9: then Node is classified as friend and accepts the request and performs function 10: Else Signal strength of received message > fixed signal strength in radio range 11: then Node S is classified as stranger and rejects the further requests from S. 12: End

4.2 Client Puzzle Method Puzzle is basically a number that is used to check the validity of node. The difficulty level of Unicode is based on the left bit. Changes in left bits increase or decrease the

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 www.IJCSI.org difficulty of puzzles. The core idea of hello message based client puzzles scheme (MBCP) is that the larger the number of hello messages sent, the sender will have to solve more difficult puzzles. Hence the difficulty of puzzles for stranger will increase according to number of hello messages sent. Each node has a counter to count the hello message in allotted time and a puzzle generating capability. If any node sends x hello message then it has to solve pth level difficult puzzles. For example A, B, C are three nodes that send x1, x2, x3(x1=0 & maxcon>0 The function hypo and mero returns the number of hyponyms and meronyms of a given concept c. Note that concepts representing leaves in the taxonomy will have an IC of one, since they do not have hyponyms. The value of one states that a concept is maximally expressed and cannot be further differentiated. Moreover maxcon is a constant that indicates the total number of concepts in the considered taxonomy.

Player is the member of Team (Member-of)

The Existing Similarity methods do not consider the Holonymy/Meronymy relationships defined in Wordnet. Hence we propose to devise a new similarity measure which considers these relationships and experimentally evaluate and compare it with the existing similarity measures using R&G data set and extended data set. The fig.1 shows the semantic similarity system architecture. Google based corpus Reuters Corpus Brown Corpus

Compute Frequency of Wordnet Concepts Semantic Similarity computation Resnik Jiang & Conrath Lin Pirro&Seco

Human Judgments for Extended R&G Data set

According to our new formula, the similarity between the two concepts c1 and c2 can be defined as, Simext(c1,c2) = IC (LCS (c1,c2))-hyper (c1 c2)

----(17)

where, the function LCS finds the lowest common subsumer of the two concepts c1 and c2 and the function hyper finds all the hypernyms of c1 and c2 upto the LCS node. Compute Correlation Coefficient

WordNet Taxonomy Rubenstein & Goodenough Extended DataSet

5.3 PROPOSED SIMILARITY FUNCTION BASED ON PROPOSED IC

Performance Analysis

Fig. 1 Semantic Similarity System Architecture

5.2 Proposed IC Calculation Involving Meronym The intrinsic IC for a concept c is defined as:

The Proposed IC formula will be used in existing semantic similarity methods such as Resnik, Lin and Jiang and cornath for computation of similarity measure suing R&G and M&C data sets. The influence of meronomy and hyponomy relations in calculation of similarity measure will be studied. The proposed similarity function will be tested with R&G and M&C data sets. The proposed similarity function with noun pairs of R&G data set was tested for Resnik method and the correlation coefficient with human judgment were calculated. The results were promising. We have to test the proposed similarity measure against other data set. Most of the IC based approaches are tested against the well known corpus. The concepts not present in Brown

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IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 11, May 2010 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814

news groups and google. Input (noun1, noun2), Output (similarity score) Step 1: For each sense c1 and c2 in noun1 and noun2 If c1 and c2 are hyponyms Calculate IC 5.4 (c1)Proposed for hyponyms i.e Algorithm

In WordNet taxonomy they do not take into consideration all the relation like meronymy.Since we are considering all the relationship present in the WordNet taxonomy, the new metric gives accurate results.

IC(c) = 1- log(hypo(c)+1)

6. Conclusion

log(maxcon) go to step 3 Else if c1 and c2 are meronyms Calculate IC(c1) for meronyms i.e IC(c) = 1- log(mero(c)+1) log(maxcon) go to step 3 Step 2: For IC value for both hyponyms and meronyms using the proposed IC formula,

This paper has discussed the various approaches that could be used for finding similar concepts in an ontology and between ontologies. We have done a survey to exploit the similarity methods for ontology based query expansion to aid better retrieval effectiveness of Information retrieval models. The experiments conducted by early researches provide better correlation values which gives promising direction of using them in Ontology based retrieval models. A new semantic similarity metric has been introduced which overcomes the shortcomings of existing semantic similarity methods mainly sparse data problem. We are working with the new similarity function which will combine the advantages of the similarity methods discussed in this paper and we will test it with ontologies of particular domain.

IC(c) = 1- log(hypo(c)+mero(c)+1)

7. REFERENCES

log(maxcon) [1]

go to step 3

Roy Rada, H. Mili, Ellen Bicknell, and M. Blettner. “Development and application of a metric on semantic nets”

Step 3: Call the existing semantic similarity function Simres(c1,c2), SimLin(c1,c2), SimJ&C(c1,c2)

IEEE Transactions on Systems, Man, and Cybernetics, 19(1), 17{30}, January 1989. [2]

And then go to step 4. Step 4: Call the proposed semantic similarity function for the given concepts c1 & c2

[3]

Michael Sussna: “Word sense disambiguation for tree-text indexing using a massive semantic network” In Bharat Bhargava Zhibiao Wu and Martha Palmer. “Verbs semantics and lexical selection”, In Proceedings of the 32nd annual meeting on Association for Computational Linguistics”, Association for Computational Linguistics, 1994. pages 133{138, Morristown, NJ, USA

Simext(c1,c2) = IC(LCS(c1,c2))-

[4]

Christiane Fellbaum, editor, WordNet: An Electronic Lexical Database. MIT Press, 1998.

hyper(c1 c2) [5]

Step 5: Collect human judgments and save it as a separate table for the R&G and M&C data sets

corpus may be present in alternate corpora like Reuters 20

Philip Resnik.: “Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language”, 1999.

[6]

George A. Miller, Richard Beckwith, Christiane Fellbaum, Derek Gross, and K.J. Miller.[Resnik, 1995] Philip Resnik.

Step 6: Calculate the correlation coefficients between results of the similarity measures and human judgments Step 7: Compare the similarity measures for R&G data set using the proposed IC and proposed similarity existing similarity measures.

Claudia Leacock and Martin Chodorow: “Combining local context and wordnet similarity for word sense identification”. In

“Using information content to evaluate semantic similarity in a taxonomy”. In IJCAI, pages 448{453, 1995. [7]

D.

Conrath

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Jiang.:

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similarity

based

on

corpusstatistics and lexical taxonomy”. In Proceedings on

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in

Computational

[8] Dekang Lin.: “An information-theoretic definition of A similarity”. In Jude W. Shavlik, editor, ICML, pages 296{304. Morgan Kaufmann, 1998. ISBN 1-55860-556-8. [9]

Graeme Hirst and David St-Onge: “Lexical chains as representation of context for the

detection and correction

malapropisms” In Christiane Fellbaum, editor, WordNet: An Electronic Lexical Database. MIT Press, 1998. [10] Troels Andreasen, Rasmus Knappe, and Henrik Bulskov: ” Domain-specifiic similarity and retrieval”. In Yingming Liu, Guoqing Chen, and Mingsheng Ying, editors, 11th International Fuzzy Systems Association World Congress, volume 1, pages 496{502, Beijing, University Press.

China,

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[11] Rubinstein and Goodenough,

IFSA

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Goodenough: “Contextual correlates Communications of the ACM, 8(10), 1965.

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[12] George A. Miller and W. G. Charles: “Contextual correlates of semantic similarity”. Language and Cognitive Processes, 6(1):1{28, 1991. [13] Dekang Lin: “Using syntactic dependency as local context to resolve word sense ambiguity”. In ACL, pages 64{71, 1997. [14] Roy Rada and Ellen Bicknell: “Ranking documents with a thesaurus”.JASIS, Yelena Yesha, Conference on a.

40(5):304{310,1989.Timothy

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Management, pages 67{74, New York, NY, USA, November 1993. ACM Press.

[15] Alexander Budanitsky: “University of Toronto Graeme Hirst, University of Toronto Evaluating WordNet-based Measus Of Lexical Semantic Relatedness”, Association for Computational Linguistics,2006 [16]

Philip Resnik: “Using Information Content to Evaluate Semantic Similarity in a Taxonomy”, Sun Microsystems Laboratories, 1995

[17]

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Bulskov

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Retrieval Computer Science Section Roskilde University”, 1996 [18] A. Budanitsky. “Lexical semantic relatedness and its application in

natural

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[19] A. Budanitsky. Semantic distance in wordnet: An experi mental, application-oriented

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[20] H. Bulskov: “Ontology-based Information Retrieval”, PhD Thesis. Andreasen, T., Bulskov, H., & Knappe, R. (2006). [21] N. Seco, T. Veale, J. Hayes: “ An intrinsic information content metric for semantic similarity in WordNet, in : Proceedings of ECAI, 2004, pp. 1089–1090. [22] Giuseppe Pirró: “A semantic similarity metric combining eatures and intrinsic information content”, Data & Knowledge Engineering 68(2009) 1289–1308 2009.

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IJCSI CALL FOR PAPERS NOVEMBER 2010 ISSUE Volume 7, Issue 6 The topics suggested by this issue can be discussed in term of concepts, surveys, state of the art, research, standards, implementations, running experiments, applications, and industrial case studies. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas. See authors guide for manuscript preparation and submission guidelines. Accepted papers will be published online and authors will be provided with printed copies and indexed by Google Scholar, Cornell’s University Library, ScientificCommons, CiteSeerX, Bielefeld Academic Search Engine (BASE), SCIRUS and more. Deadline: 30th September 2010 Notification: 31st October 2010 Revision: 10th November 2010 Online Publication: 30th November 2010                

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Software development and deployment Knowledge virtualization Systems and networks on the chip Context-aware systems Networking technologies Security in network, systems, and applications Knowledge for global defense Information Systems [IS] IPv6 Today - Technology and deployment Modeling Optimization Complexity Natural Language Processing Speech Synthesis Data Mining

For more topics, please see http://www.ijcsi.org/call-for-papers.php All submitted papers will be judged based on their quality by the technical committee and reviewers. Papers that describe research and experimentation are encouraged. All paper submissions will be handled electronically and detailed instructions on submission procedure are available on IJCSI website (www.IJCSI.org). For more information, please visit the journal website (www.IJCSI.org)

© IJCSI PUBLICATION 2010 www.IJCSI.org

IJCSI The International Journal of Computer Science Issues (IJCSI) is a refereed journal for scientific papers dealing with any area of computer science research. The purpose of establishing the scientific journal is the assistance in development of science, fast operative publication and storage of materials and results of scientific researches and representation of the scientific conception of the society. It also provides a venue for researchers, students and professionals to submit ongoing research and developments in these areas. Authors are encouraged to contribute to the journal by submitting articles that illustrate new research results, projects, surveying works and industrial experiences that describe significant advances in field of computer science.

Indexing of IJCSI: 1. Google Scholar 2. Bielefeld Academic Search Engine (BASE) 3. CiteSeerX 4. SCIRUS 5. Docstoc 6. Scribd 7. Cornell’s University Library 8. SciRate 9. ScientificCommons

© IJCSI PUBLICATION www.IJCSI.org