Self-adaptive Monte Carlo localization algorithm of mobile nodes in WSN

To overcome the disadvantages of low localization accuracy and poor efficiency of Monte Carlo Localization(MCL)algorithm in harsh Wireless Sensor Network(WSN),a self-adaptive localization algorithm based on MCL is proposed.The sample particles in different regions have different effects on unknown node localization accuracy.The proposed algorithm assigns self-adaptive weights to sample particles in different regions to position the unknown nodes.At the same time,the algorithm adds the constraint condition using the last-time sample particles.Simulation results show that the average localization error of the proposed self-adaptive MCL algorithm descends 13% at different degrees of irregularity.The average localization error descends about 10%at different node speeds.The network coverage rate reaches 99.19%.