引入虚拟节点的无线传感器网络ELM定位算法  

ELM localization algorithm introducing virtual nodes for wireless sensor networks

作　　者：刘国繁[1] 赵天霓 LIU Guo-fan;ZHAO Tian-ni(School of Electrical Information, Hunan Institute of Engineering, Xiangtan 411104, China;College of Information Engineering,Xiangtan University,Xiangtan 411105,China)

机构地区：[1]湖南工程学院电气信息学院,湖南湘潭411104 [2]湘潭大学信息工程学院,湖南湘潭411105

出　　处：《传感器与微系统》2018年第4期125-128,共4页Transducer and Microsystem Technologies

基　　金：广东省普及型高性能计算机重点实验室开放课题资助项目(SZU-GDPHPCL201303);湖南省科技计划资助项目(2012SK3173)

摘　　要：基于大部分距离无关算法能以改善锚节点比例提高无线传感器网络定位精度,提出了一种引入虚拟节点的无线传感器网络极限学习机(ELM)定位算法。通过引入的虚拟节点,寻找合适的未知节点升级为次锚节点,以增加锚节点比例,提高了定位精度。将ELM应用于节点定位,有效提高了定位的速度和精度,并因其强大的泛化性能,为无线传感器网络节点定位提供了新的思路。仿真结果表明:引入ELM定位算法和虚拟节点,有效提高了定位精度。Based on the fact that most distance-free algorithms can improve anchor node ratio to improve the precision of wireless sensor networks node localization,an extreme learning machine（ELM） localization algorithm introducing virtual nodes for wireless sensor networks is proposed. Through introducing virtual nodes,looking for suitable unknown nodes to upgrade to sub-anchor nodes,so as to increase proportion of anchor nodes and improve positioning precision.Applying ELM to node localization can effectively improve speed and precision of localization and its powerful generalization performance can provide new ideas for localization of nodes in wireless sensor networks.The simulation result indicates that introducing ELM localization algorithm and virtual nodes can improve positioning precision effectively.

关 键 词：无线传感器网络 极限学习机 次锚节点 虚拟节点 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]