Monte Carlo Location Algorithm Based on Model Prediction

Location is the core of a wireless sensor network that provides basic information. Many existing algorithms focus on studying static networks while ignoring or not considering their mobility. The Monte Carlo Iocalization (MCL) algorithm is the more prominent one in the dynamic network positioning algorithms. MCL positioning algorithm is in the context of self-organizing network, by the wireless sensor network technology, to locate the nodes in the dynamic network. This paper mainly introduces the Monte Carlo Iocalization algorithm, and proposes an improved Iocalization algorithm based on the Monte Carlo positioning algorithm combined with the establishment of motion model of the networks. The improved algorithm is based on the position of the node obtained by the Monte-Carlo Localization Boxed (MCB), and then the mobile node establishes a fast and effective prediction model to compare and screen, so that the relevant communication cost for the mobile nodes will be greatly reduced, and a series of problems brought about by mobility will also be simplified. The simulation results show that the improved algorithm can improve the positioning accuracy to a certain extent, and it is with more feasibility.