Nonparametric Boxed Belief Propagation for Localization in Wireless Sensor Networks

Obtaining estimates of each sensorpsilas position as well as accurately representing the uncertainty of that estimate is a critical step for effective application of wireless sensor networks (WSN). Nonparametric belief propagation (NBP) is a popular localization method which uses particle based approximation of belief propagation. In this paper, we present a new variant of NBP method which we call nonparametric boxed belief propagation (NBBP). The main idea is to constraint the area from which the samples are drawn by building a box that covers the region where anchorspsila radio ranges overlap. These boxes, which are created almost without any additional communication between nodes, are also used to filter erroneous samples of the beliefs. In order to decrease the computational and the communication cost, we also added incremental approach using Kullback-Leibler (KL) divergence as a convergence parameter. Simulation results show that accuracy, computational and communication cost are significantly improved.