摘要
无线传感器网络具有感知和处理信息的能力,只有当被测网络内节点的位置已知时,节点传递给用户的信息才有意义。针对DV-Hop定位中传统最小二乘法不可避免的精度低的缺点,引入粒子群算法(PSO)和灰狼优化器(GWO)来估计未知节点位置。粒子群算法具有个体记忆的特点,采用粒子位置更新代替灰狼个体位置更新,使灰狼算法在优化上具有可记忆性。仿真数据表明,改进后的算法可以有效降低节点定位误差,实现更高的定位精度。
Wireless sensor networks have the ability to sense and process information,and the information passed by the nodes to the user is meaningful only when the location of the nodes within the network under test is known.In view of the inevitable shortcoming of low precision of the traditional least squares method in DV-Hop(distance vector-hop)localization,the Particle Swarm optimization(PSO)and the Gray Wolf Optimizer(GWO)are introduced to estimate unknown node positions.The Particle Swarm optimization has the characteristics of individual memory,and the particle position update is used to replace the gray wolf individual position update,so that the gray wolf algorithm has memory in optimization.The simulation data show that the improved algorithm can effectively reduce the node positioning error and achieve higher positioning accuracy.
作者
朱子行
陈辉
ZHU Zihang;CHEN Hui(Anhui University of Science&Technology,Huainan 232001,China)
出处
《现代信息科技》
2022年第3期88-91,共4页
Modern Information Technology
作者简介
朱子行(1998-),男,汉族,安徽淮北人,在读硕士,研究方向:无线传感器网络定位方向;通讯作者:陈辉(1973-),男,汉族,安徽庐江人,副教授,硕士生导师,博士,研究方向:无线传感器网络,机器学习、物联网技术及应用。
