基于粒子群进化的输电网络WSN节点定位算法  

WSN node localization algorithm for power transmission networks based on particle swarm optimization

作　　者：任鹏飞 谷灵康 REN Peng-fei;GU Ling-kang(College of Electrical Information and Engineering,Henan Institute of Engineering,Zhengzhou 451191,China;School of Computer and Information,Anhui Polytechnic University,Wuhu 241000,China)

机构地区：[1]河南工程学院电气信息工程学院,郑州451191 [2]安徽工程大学计算机与信息学院,安徽芜湖241000

出　　处：《沈阳工业大学学报》2018年第5期541-546,共6页Journal of Shenyang University of Technology

基　　金：国家自然科学基金资助项目(51407035);广东省自然科学基金资助项目(S2013040013776);河南省高等学校重点科研项目(17A470007)

摘　　要：为了提升WSN的定位精度,提出了一种基于粒子群进化的定位算法,以应用于输电网络中的节点定位.该算法通过区域估计,缩小并限制传感器节点的预估计区域空间,并应用粒子群算法快速寻找节点定位的最优解.通过引入权重自适应的机制,加快节点定位的搜索速度,并提升算法的搜索能力.结果表明,该算法有效增强了WSN节点定位的精度,降低了计算复杂度,为输电网络的无线传感器网络提供更高效准确的定位服务.In order to improve the node localization accuracy of WSN,a localization algorithm based on particle swarm optimization(PSO)was proposed and applied to the node localization in the power transmission networks.Through the regional estimation,the pre-estimation regional space of sensor nodes was reduced and limited,and the optimal solution of node localization was quickly searched with the PSO algorithm.Moreover,the search capacity of the algorithm was improved.The results show that the proposed algorithm can greatly promote the accuracy of WSN node localization,reduce the computational complexity,and provide more accurate service for the node localization of wireless sensor networks(WSN).

关 键 词：输电网络 节点定位 粒子群 无线传感器网络 测距 适应度 区域估计 权重 

分 类 号：TN393[电子电信—物理电子学]