Collaborative Localization Method in Wireless Sensor Networks: A Game Theoretic Perspective

Most of the localization algorithms use neighbor nodes and anchor nodes to refine the position of nodes estimation. Nevertheless, there has not been any paper mentioned the problem of how to refine the position of nodes in a most rational way. The GTCMS (Game theory based confidence mass-spring) algorithm proposed in our paper elegantly solved this problem using game theory. In the position refining procedure of GTCMS, each node first selects the neighbor nodes which will be used to adjust its position by following the natural game course users converge to a Nash equilibrium, and then uses the best response theorem we proposed to adjust its position. We also prove that the GTCMS algorithm could achieve the global Nash equilibrium. Compared with traditional localization methods, GTCMS algorithm greatly reduces the localization error rate and converges much more efficiently.