基于IPSO-MC融合算法的无线传感器节点定位

doi:10.11959/j.issn.1000-0801.2020063

Abstract

Abstract:

To solve the problem of insufficient node positioning accuracy in wireless sensor networks,an algorithm based on improved particle swarm optimization by membrane computing (IPSO-MC) was proposed.Kent mapping was used to initialize the population and domain particles were introduced to improve the global optimization of the particle swarm.The weight factor and nonlinear extreme value perturbation were used to improve the local optimization ability of the particle swarm,and the Levy flight mechanism was used to optimize the individual position.Finally,the optimal solution of the particle swarm algorithm was obtained by the evolutionary rules of the membrane computing.Simulation experiments show that compared with the chicken flock algorithm,the improved particle swarm algorithm and the membrane computing,the proposed algorithm improves 3.24%,5.12% and 8.15% in the comparison of reference node ratio indicators,and the increase in the number of nodes indicators by 2.26%,7.82% and 9.81%,and the comparison of communication radius indicators increased by 2.15%,5.5% and 7.5%,respectively.This indicates that the algorithm has a good effect in node localization.

Key words: wireless sensor network, node location, particle swarm algorithm, membrane computing

CLC Number:

TP391

Yongyan LI,Jianping WU. Wireless sensor node localization based on IPSO-MC[J]. Telecommunications Science, 2020, 36(3): 11-18.

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References 15

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参数名称	参数数值
区域大小	10 m×10 m
总节点数/个	50
参考节点数/个	20
通信半径/m	30
节点分布	随机分布

Wireless sensor node localization based on IPSO-MC

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