面向环境监测的WSN节点定位技术研究

doi:10.11896/j.issn.1002-137X.2018.03.015

摘要/Abstract

摘要： WSN节点定位在无线传感器网络研究中意义非凡,设计出一种精确的定位算法是当今的重大挑战。传感器节点采集的数据只有在获取到节点的位置信息后才有意义,结合环境监测特点和应用需求,DV-Hop(Distance Vector-Hop)算法因其受环境影响相对较小,无需大量硬件开销,适用于环境监测场景。针对传统DV-Hop算法定位精度不高的问题,提出基于加权因子的混合DV-Hop算法——HDV-Hopw,其采用两种策略对传统DV-Hop算法进行改进。首先,通过对信标节点的平均每跳距离进行加权处理,减小平均每跳距离带来的误差；然后,将未知节点位置估计转换成目标优化,采用混合GA-PSO算法对未知节点的坐标进行优化,通过限制初始种群的可行域以及改进初始种群的质量来提高算法的定位精度。仿真实验结果表明,在没有增加额外硬件设备的情况下, 相比于DV-Hop算法 ,HDV-Hopw算法的定位误差平均降低了11%左右。

关键词: 无线传感器网络,定位,DV-Hop,加权因子,遗传算法,粒子群优化算法

Abstract: WSN node localization is of great significance in wireless sensor network research.The data collected by sensor nodes with node location is more meaningful.Combining environmentent monitoring features and application requirements ,DV-Hop (Distance Vector-Hop) algorithm is suitable in environmentent monitoring scene because of its small environmentent impact and little hardware overhead.Aiming at the shortage of traditional DV-Hop algorithm,this paper proposed a hybrid DV-Hop algorithm based on weighted factor,called HDV-Hopw,which is improved by two methods.Firstly,the average per-hop distance of beacon node is weighted to reduce the error caused by the average distance per hop.Then,position estimation of unknown nodes is transformed into objective optimization,and GA-PSO algorithm is used to optimize the coordinates of unknown nodes.The feasible region of the initial population is restricted and initial population is improved to improve the position accuracy of the algorithm.The simulation results show that compared with the DV-Hop algorithm,the localization error of HDV-Hopw is reduced by about 11% without increasing the hardware cost.

Key words: Wireless sensor network,Localization,DV-Hop,Weighted factor,Genetic algorithm,Particle swarm optimization algorithm

杨佩茹,薛善良. 面向环境监测的WSN节点定位技术研究[J]. 计算机科学, 2018, 45(3): 92-97. https://doi.org/10.11896/j.issn.1002-137X.2018.03.015

YANG Pei-ru and XUE Shan-liang. Study on WSN Node Localization Technology for Environment Monitoring[J]. Computer Science, 2018, 45(3): 92-97. https://doi.org/10.11896/j.issn.1002-137X.2018.03.015

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