基于改进粒子群算法的无线传感器网络节点定位

doi:10.11772/j.issn.1001-9081.2015.06.1519

计算机应用 ›› 2015, Vol. 35 ›› Issue (6): 1519-1522.DOI: 10.11772/j.issn.1001-9081.2015.06.1519

基于改进粒子群算法的无线传感器网络节点定位

于泉¹, 孙顺远¹, 徐保国¹, 陈淑娟²

1. 江南大学物联网工程学院, 江苏无锡 214122;
2. 恒启电子(苏州)有限公司, 江苏苏州 215000

收稿日期:2014-12-26 修回日期:2015-02-06 出版日期:2015-06-10 发布日期:2015-06-12
通讯作者: 于泉(1990-),男,江苏徐州人,硕士研究生,主要研究方向:无线传感器网络;is_chou_yu@sina.cn
作者简介:孙顺远(1984-),男,山东淄博人,博士研究生,主要研究方向:无线传感器网络;徐保国(1951-),男,江苏淮安人,教授,博士生导师,主要研究方向:无线传感器网络、现场总线;陈淑娟(1989-),女,江苏徐州人,工程师,主要研究方向:路由器。
基金资助:
江苏省研究生培养创新工程项目(CXZZ11_0465);江南大学博士研究生科学研究基金资助项目(JUDCF11003)。

Node localization in wireless sensor networks based on improved particle swarm optimization

YU Quan¹, SUN Shunyuan¹, XU Baoguo¹, CHEN Shujuan²

1. School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China;
2. Henrich Electronic (Suzhou) Company Limited, Suzhou Jiangsu 215000, China

Received:2014-12-26 Revised:2015-02-06 Online:2015-06-10 Published:2015-06-12

摘要/Abstract

摘要：

针对传统距离矢量-跳数(DV-Hop)算法中最小二乘法的估计误差过大、粒子群(PSO)算法易陷入局部最优的问题,提出了一种改进粒子群算法与DV-Hop的融合算法。首先从粒子速度、惯性权重、学习策略、变异方面对粒子群算法进行改进,增强算法跳出局部最优的能力,提高迭代后期算法的搜索速度;然后在DV-Hop算法第三阶段采用改进粒子群算法优化节点的定位结果。仿真结果表明:相比传统DV-Hop算法、基于混沌粒子群算法的DV-Hop改进算法(MPSO1-DV-Hop)和基于改进型粒子群优化的DV-Hop算法(MPSO2-DV-Hop),该算法的定位精度高,稳定性好,适用于定位精度和稳定性要求较高的场景。

关键词: 无线传感器网络, 粒子群算法, 距离矢量-跳数算法, 惯性权重, 变异

Abstract:

The estimation error of the least square method in traditional Distance Vector-Hop (DV-Hop) algorithm is too large and the Particle Swarm Optimization (PSO) algorithm easily traps into local optimum. In order to overcome the problems, a fusion algorithm of improved particle swarm algorithm and DV-Hop algorithm was presented. First of all, PSO algorithm was improved from aspects of particle velocity, inertia weight, learning strategy and variation, which enhanced the ability of algorithm to jump out of local optimum and increased the search speed of the algorithm in later iterative stage. The node localization result was optimized by using the improved PSO algorithm in the third stage of the DV-Hop algorithm. The simulation results show that compared with the traditional DV-Hop algorithm,the improved DV-Hop based on chaotic PSO algorithm, and the DV-Hop algorithm based on improved PSO, the proposed algorithm has high positioning accuracy, good stability.

Key words: Wireless Sensor Network (WSN), Particle Swarm Optimization (PSO) algorithm, Distance Vector-Hop (DV-Hop)algorithm, inertia weight, variation

中图分类号:

TP393

于泉, 孙顺远, 徐保国, 陈淑娟. 基于改进粒子群算法的无线传感器网络节点定位[J]. 计算机应用, 2015, 35(6): 1519-1522.

YU Quan, SUN Shunyuan, XU Baoguo, CHEN Shujuan. Node localization in wireless sensor networks based on improved particle swarm optimization[J]. Journal of Computer Applications, 2015, 35(6): 1519-1522.

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基于改进粒子群算法的无线传感器网络节点定位

Node localization in wireless sensor networks based on improved particle swarm optimization

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