Resilient node self-positioning methods for MANETS based on game theory and genetic algorithms

We present a distributed and scalable game participated by autonomous MANET nodes to place themselves uniformly over a dynamically changing environment. A node spreading potential game, called Rel-NSPG, run at each node, autonomously makes movement decisions based on localized data while the best next location to move is selected by a genetic algorithm (GA). Since it requires only a limited synchronization among the closest neighbors of a player, and does not require a priori knowledge of the environment, Rel-NSPG is a good candidate for node spreading class of applications used in military tasks. The performance of Rel-NSPG degrades gracefully when the number of MANET nodes decrease either due to equipment malfunction or hostile activities. We show that this resilience to loss of nodes is inherent in Rel-NSPG. Simulation experiments demonstrate that, after a subset of the MANET nodes arbitrarily become unavailable, the remaining nodes recover and offset lost nodes. Similarly, when there are losses concentrated in a given region, remaining nodes reconfigure their positions to compensate for the missing area coverage. The simulation experiments with arbitrarily placed obstacles, in addition to lost assests, produce promising results.