An Enhanced Monte Carlo Localization Algorithm for Mobile Node in Wireless Sensor Networks

There are some common problems, such as low sampling efficiency and large amount of calculation, in mobile localization algorithm based on Monte Carlo localization (MCL) in wireless sensor networks. To improve these issues, an enhanced MCL algorithm is proposed. The algorithm uses the continuity of the nodes movement to predict the area where the unknown node may reach, constructs high posteriori density distribution area, adds the corresponding weights to the sample points which fall in different areas, and filters the sample points again by using the position relations between the unknown node and its one-hop neighbors which include anchor nodes and ordinary nodes. Simulation results show that the localization accuracy of the algorithm is superior to the traditional localization algorithm. Especially when the anchor node density is lower or the unknown nodes speed is higher, the algorithm has higher location accuracy.