Markov chain models for genetic algorithm based topology control in MANETs

Authors:
Cem Şafak Şahin;Stephen Gundry;Elkin Urrea;M. Ümit Uyar;Michael Conner;Giorgio Bertoli;Christian Pizzo
Affiliations:
The City College and Graduate Center of the City University of New York, NY;The City College and Graduate Center of the City University of New York, NY;The City College and Graduate Center of the City University of New York, NY;The City College and Graduate Center of the City University of New York, NY;The City College and Graduate Center of the City University of New York, NY;US Army Communications-Electronics RD&E Center, Fort Monmouth, NJ;US Army Communications-Electronics RD&E Center, Fort Monmouth, NJ
Venue:
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
Year:
2010

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Abstract

We analyze the convergence properties of our force based genetic algorithm(fga) as a decentralized topology control mechanism distributed among software agents. fga guides autonomous mobile agents over an unknown geographical area to obtain a uniform node distribution. The stochastic behavior of fga makes it difficult to analyze the effects of various manet characteristics over its convergence rate. We present ergodic homogeneous Markov chains to analyze the convergence of our fga with respect to changing communication range of mobile nodes. Simulation experiments indicate that the increased communication range for the mobile nodes does not result in a faster convergence.