基于随机微粒群算法的分布式节点定位方法

J4 ›› 2012, Vol. 47 ›› Issue (9): 51-55.

基于随机微粒群算法的分布式节点定位方法

崔焕庆^1,2,王英龙^1*,吕家亮^1,2,魏诺¹

1. 山东省计算机网络重点实验室,山东省计算中心, 山东济南250014;
2. 山东科技大学信息科学与工程学院, 山东青岛 266590

收稿日期:2011-06-13 出版日期:2012-09-20 发布日期:2012-09-24
通讯作者: 王英龙(1965- ),男,博士,研究员,博士生导师,研究方向为计算机网络、信息安全等.Email:wangyl@keylab.net
作者简介:崔焕庆(1979- ),男,博士,讲师,研究方向为无线传感器网络、高性能计算等.Email:cuihq@keylab.net
基金资助:
国家自然科学基金资助项目(60802030);山东省自然科学基金资助项目(ZR2009GQ002,ZR2010FQ014);山东科技大学科学研究“春蕾计划”项目(2010AZZ175)

Distributed node localization based on the stochastic particle swarm optimization

CUI Huan-qing1,2, WANG Ying-long1*, L Jia-liang1,2, WEI Nuo1

1. Shandong Provincial Key Laboratory of Computer Network, Shandong Computer Science Center, Jinan 250014,
Shandong, China; 2. College of Information Science and Engineering, Qingdao 266590, Shandong, China

Received:2011-06-13 Online:2012-09-20 Published:2012-09-24

摘要/Abstract

摘要：

提出了基于随机微粒群优化算法的定位方法。设定网络中存在部分锚节点,且相邻节点之间可以获取距离信息,待定位节点在获取足够的相邻锚节点或已定位节点的距离、位置信息后,使用随机微粒群优化算法实现定位。仿真表明,该方法比多边测量法和基于标准微粒群优化算法的定位方法具有更高的性能。

关键词: 无线传感器网络;定位;测距;随机微粒群优化

Abstract:

A localization method based on stochastic particle swarm optimization is proposed. Suppose there are some anchor nodes in the network, and the distance between adjacent sensor nodes can be measured, so the sensor nodes to be localized utilize stochastic particle swarm optimization to estimate positions after obtaining enough distances and positions of neighboring anchors or localized sensor nodes. Simulation shows that this method outperforms multilateration and localization based on the standard particle swarm optimization.

Key words: wireless sensor networks; localization; range; stochastic particle swarm optimization

崔焕庆1,2,王英龙1*,吕家亮1,2,魏诺1. 基于随机微粒群算法的分布式节点定位方法[J]. J4, 2012, 47(9): 51-55.

CUI Huan-qing1,2, WANG Ying-long1*, L Jia-liang1,2, WEI Nuo1. Distributed node localization based on the stochastic particle swarm optimization[J]. J4, 2012, 47(9): 51-55.

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