A new variant of sum-product algorithm for sensor self-localization in wireless networks

Graphical models have been widely applied in solving distributed inference problems in wireless sensor networks (WSNs). In this paper, we formulate the sensor self-localization problem in a WSN as an inference problem on a factor graph. Using a sequential schedule of message updates, a sequential uniformly reweighted sum-product algorithm (SURW-SPA) is developed for self-localization problems. The proposed algorithm combines the distributed nature of belief propagation (BP) with the improved performance of sequential tree-reweighted message passing (TRW-S) algorithm. We apply the SURW-SPA to sensor self-localization in large-scale static networks, and evaluate its performance in terms of localization accuracy and convergence speed.