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

  

  • Received:2013-02-06 Revised:2013-04-23 Published:2013-06-20
  • Contact: Jian-Po LI

Abstract: Aiming at the low localization accuracy and poor efficiency of Monte Carlo Localization (MCL) algorithm in harsh wireless sensor network (WSN), proposed a self-adaptive localization algorithm based on MCL. The sample particles in different region had different effects on unknown node localization accuracy. The algorithm assigned self-adaptive weights to sample particles in different regions to position the unknown nodes. At the same time, the algorithm added the limit condition using the last-time sample particles. Simulation results show that the average localization error of new MCL algorithm descends 13% at different degree of irregular. The average localization error descends about 10% at different speed of nodes. The network coverage rate reaches 99.19%.

Key words: communication, mobile node localization, MCL algorithm, wireless sensor network

CLC Number: 

  • TP393
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