An efficient localization algorithm for Mobile Wireless Sensor Networks

Localization is one of fundamental issues in Wireless Sensor Networks(WSNs). In mobile WSNs, locating sensor nodes is more challenge than that in static WSNs because of the uncertainty of the node movement. Most of existing mobile network localization algorithms are based on Sequential Monte Carlo(SMC) methods, which have weaknesses of inefficient sampling and depending on anchor sensor node density. In this paper, an improved localization scheme is proposed to overcome weaknesses and improve localization accuracy. We take the location information of common nodes into consideration, and use it as a constraint to build a more accurate bounding box, which can reduce sampling area. This proposed scheme introduces genetic algorithm in the sampling process to improve sampling efficiency. Virtual anchor nodes are used to improve localization accuracy in the situation of low anchor node density. The simulation results and analysis show that the proposed scheme performs better in the localization accuracy and communication cost compared with the existing ones, even when there are a few anchor nodes.