改进的粒子群蒙特卡洛WSN节点定位算法

doi:10.11896/jsjkx.210900156

摘要/Abstract

摘要： 无线传感器网络是由监测范围内的节点构成而且能够相互通信的自组织网络。针对传统粒子群蒙特卡洛算法存在定位时间长、定位精度低的问题,提出了一种改进的粒子群蒙特卡洛定位算法(IPSOMCL)。利用蒙特卡洛算法获取待定位节点的估计坐标,通过粒子群算法修正估计距离与测量距离的误差。在改进过滤阶段,提取锚节点信息的跳数得到一个精度更高的采样区域代替传统算法通过通信半径确定采样区域的方式进行过滤。引入交叉变异使算法能够跳出局部最优解并找到更加准确的位置坐标节点,提高定位的效率和定位精度。

关键词: 无线传感器网络, 蒙特卡洛算法, 粒子群算法, 环形采样, 交叉变异

Abstract: Wireless sensor network(WSN) is a self-organizing network that is composed of nodes within the monitoring range and can communicate with each other.In view of the long location time and low location accuracy of the traditional particle swarm Monte Carlo algorithm,an improved particle swarm Monte Carlo positioning algorithm is proposed.(IPSOMCL).The Monte Carlo algorithm is used to obtain the estimated coordinates of the node to be located,and the particle swarm algorithm is used to correct the error between the estimated distance and the measured distance.Toimprove the filtering stage,extracting the number of hops of anchor node information to obtain a more accurate sampling area instead of the traditional algorithm to determine the sampling area through the communication radius to filter.The introduction of cross mutation enables the algorithm to jump out of the local optimal solution and find a more accurate position coordinate node,which improves the efficiency and accuracy of positioning.

Key words: Wireless sensor network, Monte Carlo algorithm, Particle swarm algorithm, Circular sampling, Cross mutation

中图分类号:

TP393

王灵矫, 方凯鹏, 郭华. 改进的粒子群蒙特卡洛WSN节点定位算法[J]. 计算机科学, 2022, 49(11A): 210900156-5. https://doi.org/10.11896/jsjkx.210900156

WANG Ling-jiao, FANG Kai-peng, GUO Hua. Improved Particle Swarm Monte Carlo WSN Node Location Algorithm[J]. Computer Science, 2022, 49(11A): 210900156-5. https://doi.org/10.11896/jsjkx.210900156

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