Published In : IJCSN Journal Volume 5, Issue 4

Date of Publication : August 2016

Pages : 653-660

Figures :06

Tables : --

 

Kirti Aniruddha Adoni : is presently working as Assistant Professor at P.E.S. Modern College of Engineering, Shivajinagar, Pune, India 411005. She is a Research Student of Electronics and Telecommunication department, V.I.I.T. Research Centre, Kondhwa, Pune, India. She has completed her M.Tech (Microwave) from College of Engineering, Pune, Maharashtra, India in 2010. Her major fields of study are wireless networks, mobile ad-hoc networks.

Dr. Anil S. Tavildar : is working as Professor Emeritus in Electronics and Telecommunication Engineering at MKSSS’s Cummins college of Engineering, Pune, India. He has obtained his B.E. (Electronics and Telecommunication Engineering) from University of Pune and PhD (Communication Engineering) from Indian Institute of Technology, Delhi in 1984. He has 28 years of industrial, research and development experience and 16 years of teaching experience. His research interests are in signal processing, wireless and mobile communication systems. Prof Tavildar is Senior Member, IEEE USA, Fellow Member of IETE, India, Founder Member ICIET and Life Member of ISTE, India.

 

 

 

 

 

 

 

Probabilistic Models, Selfish Behavior Attack, Probability of Blocking, MANETs

Maliciousness due to SB attack, has been modeled using uniformly distributed continuous time random variables, Poisson and uniformly distributed discrete values for ONOFF pulses 'k' and binomially distributed number of malicious nodes 'n' of the network. Formulation for probability of network blocking for individual nodes Pbj and total network blocking probability, PB, based on concept of statistical averaging have been proposed. MATLAB based simulations on typical MANET network of fifty nodes, indicate that blocking probability is maximum around k=2 and it saturates for higher values of 'k' for uniform distribution. In the above analysis the basic occurrence of random time for ON-OFF switching of nodes has been assumed to be uniformly distributed in available time. However, any other continuous time distributions like, Gaussian, Lognormal, Exponential distribution etc. may also be used in modeling. The analysis can be carried out, using the similar approach.

 

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