Localization Based on Particle Swarm Optimization with Penalty Function for Wireless Sensor Network

WSN (wireless sensor network) is formed by a large number of cheap sensors, which communicate through an ad hoc wireless network to collect information of sensed objects of a certain area. Hence, it can be used widely in military affairs, environment detection and intelligent home. In most applications of WSN, the acquired information is useful only when the locations of sensors and objects are known. Therefore, localization is one of the most important technologies of WSN. Now, some intelligent algorithms, for example PSO (particle swarm optimization), are studied for node localization in WSN. However, the existing PSO algorithm has lower localization accuracy and convergence speed. Hence, in order to improve the convergence speed and the localization accuracy further, a new localization algorithm based on PSO with penalty function (PSOPF) is proposed in this paper. In PSOPF, an error correction factor is defined to reflect the average error of distance measure between a node and some sample anchors firstly. And then, a penalty function based on error correction factor for PSO is defined to improve its convergence speed and localization accuracy. The simulation results show that, compared with PSO location algorithms, PSOPF has higher positioning accuracy and higher convergence speed.