Distributed coverage games for mobile visual sensors (I): Reaching the set of Nash equilibria

We formulate a coverage optimization problem for mobile visual sensor networks as a repeated multi-player game. Each visual sensor tries to optimize its own coverage while minimizing the processing cost. The rewards for the sensing are not prior information for the agents. We present a synchronous distributed learning algorithm where each sensor only remembers its own utility values and actions played during the last two time steps. The algorithm is proven to be convergent in probability to the set of (restricted) Nash equilibria from which none has incentive to unilaterally deviate.