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改进粒子群算法的无线传感器网络节点定位
引用本文:王亚子,杨建辉.改进粒子群算法的无线传感器网络节点定位[J].计算机工程与应用,2014(18):99-102.
作者姓名:王亚子  杨建辉
作者单位:周口师范学院数学与统计学院
基金项目:周口师范学院青年科学基金重点资助项目(No.zksyqn201310B);河南省人民政府人才培养联合基金项目(No.U1204618).
摘    要:为了提高无线传感器节点的定位精度,针对粒子群优化算法存在的问题,提出一种改进粒子群优化算法的无线传感器网络节点定位方法。根据锚节点选择准则,把上一代和当代节点位置的平均值作为下一代目标节点的参考节点,采用改进粒子群算法对节点的定位结果进行优化,在Matlab 2012平台上进行仿真对比实验。仿真结果表明,相对于标准粒子群算法,改进粒子群算法加快了定位速度,提高了无线传感器节点定位精度,应用范围更广。

关 键 词:无线传感器网络  改进粒子群优化(MPSO)算法  分布式迭代平均定位  定位误差

Localization in wireless sensor network based on improved particle swarm optimiza-tion algorithm
WANG Yazi,YANG Jianhui.Localization in wireless sensor network based on improved particle swarm optimiza-tion algorithm[J].Computer Engineering and Applications,2014(18):99-102.
Authors:WANG Yazi  YANG Jianhui
Affiliation:(School of Mathematics and Statistics, Zhoukou Normal University, Zhoukou, Henan 466001, China)
Abstract:In order to improve the localization accuracy, a novel localization meothd in wireless sensor network based on improved particle swarm optimization algorithm is proposed. The average of the location of the node in the last generation and the location of the same node in the current generation is regarded as the current generation node location following with the criteria of choosing beacon nodes, and the improved particle swarm optimization algorithm is used to optimize the localization results. The simulation experiments are carried out. The results show that the proposed algorithm not only fastens the localization speed and has improved the localization accuracy effectively compared with the PSO algorithm, so it has wider application range.
Keywords:wireless sensor networks  Modified Particle Swarm Optimization(MPSO)algorithm  distributed iterative average localization  localization error
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