Localization in 3D Sensor Networks Using Stochastic Particle Swarm Optimization  

作　　者：ZHANG Zhangxue CUI Huanqing 

机构地区：[1]Fujian Institute of Scientific and Technology Information,Fuzhou 350003,Fujian,China [2]Fujian Strait Information Technology Co.Ltd.Fuzhou 350003,Fujian,China [3]Shandong Computer Science Center,Shandong Provincial Key Laboratory of Computer Network,Jinan 250014,Shandong,China [4]College of Information Science and Engineering,Shandong University of Science and Technology,Qingdao 266590,Shandong,China

出　　处：《Wuhan University Journal of Natural Sciences》2012年第6期544-548,共5页武汉大学学报（自然科学英文版）

基　　金：Supported by the Fujian Province University-Industry Cooperation of Major Science and Technology Project (2011H6008);the Natural Science Foundation of Shandong Province of China (ZR2009GQ002,ZR2010FQ014)

摘　　要：Localization is one of the key technologies in wireless sensor networks,and the existing PSO-based localization methods are based on standard PSO,which cannot guarantee the global convergence.For the sensor network deployed in a three-dimensional region,this paper proposes a localization method using stochastic particle swarm optimization.After measuring the distances between sensor nodes,the sensor nodes estimate their locations using stochastic particle swarm optimization,which guarantees the global convergence of the results.The simulation results show that the localization error of the proposed method is almost 40% of that of multilateration,and it uses about 120 iterations to reach the optimizing value,which is 80 less than the standard particle swarm optimization.Localization is one of the key technologies in wireless sensor networks,and the existing PSO-based localization methods are based on standard PSO,which cannot guarantee the global convergence.For the sensor network deployed in a three-dimensional region,this paper proposes a localization method using stochastic particle swarm optimization.After measuring the distances between sensor nodes,the sensor nodes estimate their locations using stochastic particle swarm optimization,which guarantees the global convergence of the results.The simulation results show that the localization error of the proposed method is almost 40% of that of multilateration,and it uses about 120 iterations to reach the optimizing value,which is 80 less than the standard particle swarm optimization.

关 键 词：wireless sensor network LOCALIZATION stochasticparticle swarm optimization 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]