An evolutionary potential game theoretic approach for the K-COVER problem in multi-UAV sensor networks

This paper focuses on the K-COVER problem in unmanned aerial vehicle (UAV) sensor networks and proposes a game theoretic approach based on the network potential game and Log-linear learning. After detailing the research progress at home and abroad, we firstly model the K-COVER problem in UAV networks as network potential game via appropriately designing of individual and global payoff functions. Secondly, a Log-linear learning based algorithm is proposed, whose convergence and optimality are proven using inhomogeneous Markov chain theory. Simulation results demonstrate the feasiblity, effectiveness, and superiority.