Self organization for area coverage maximization and energy conservation in mobile ad hoc networks

Mobile Ad hoc Networks (manets) are widely used for a large number of strategic applications from military to commercial tasks including disaster area discovery, mine field clearing, and transportation systems. In realistic applications, it is not feasible to deploy mobile nodes manually or using a centralized controller. We provide a nature-inspired approach to achieve self-organization of mobile nodes over unknown terrains. In this framework, each mobile node uses a genetic algorithm as a self-distribution mechanism to decide its next speed and movement direction to obtain a uniform distribution. We present a formal analysis of the effectiveness of our genetic algorithm and introduce an inhomogeneous Markov chain model to prove its convergence. The experiment results from our simulation software and our vmware-based testbed show that our nature-inspired algorithm delivers promising results for uniform distribution of mobile nodes over unknown terrains.