An Improved PSO Algorithm for Distributed Localization in Wireless Sensor Networks

Abstract

Accurate and quick localization of randomly deployed nodes is required by many applications in wireless sensor networks and always formulated as a multidimensional optimization problem. Particle swarm optimization (PSO) is feasible for the localization problem because of its quick convergence and moderate demand for computing resources. This paper proposes a distributed two-phase PSO algorithm to solve the flip ambiguity problem, and improve the efficiency and precision. In this work, the initial search space is defined by bounding box method and a refinement phase is put forward to correct the error due to flip ambiguity. Moreover, the unknown nodes which only have two references or three near-collinear references are tried to be localized in our research. Simulation results indicate that the proposed distributed localization algorithm is superior to the previous algorithms.