基于SSO的铀尾矿库无线传感器网络定位算法  

Localization algorithm for uranium tailings reservoir based on SSO in wireless sensor network

作　　者：余修武 彭威 余员琴 刘永 YU Xiuwu;PENG Wei;YU Yuanqin;LIU Yong(School of Resource&Environment and Safety Engineering,University of South China,Hengyang Hunan 421001,China;Hunan Engineering Research Center for Uranium Tailings Decommission and Treatment,Hengyang Hunan 421001,China;School of Electrical and Information Engineering,Hunan Institute of Traffic Engineering,Hengyang Hunan 421001,China;College of Physics and Optoelectronic Engineering,Shenzhen University,Shenzhen Guangdong 518000,China)

机构地区：[1]南华大学资源环境与安全工程学院,湖南衡阳421001 [2]湖南省铀尾矿库退役治理技术处理研究中心,湖南衡阳421001 [3]湖南交通工程学院电气与信息工程学院,湖南衡阳421001 [4]深圳大学物理与光电工程学院,广东深圳518000

出　　处：《中国安全科学学报》2023年第4期84-90,共7页China Safety Science Journal

基　　金：国家自然科学基金资助(11875164);湖南省市联合自然科学基金资助(2021JJ50093);湖南省重点研发计划项目(2018SK2055)。

摘　　要：为提高铀尾矿库无线传感器网络(WSN)定位算法的定位精度和收敛速度,利用优化的麻雀搜索算法(SSA)改进基于信号强度指示(RSSI)的定位算法。首先,引入混沌映射和精英方向学习初始化麻雀种群,丰富种群多样性,提高算法的全局寻优能力;其次,采用莱维飞行策略改进搜索者的位置更新方式,避免陷入局部最优;然后,采用优化的SSA代替最小二乘法来定位未知节点,并将定位算法应用于铀尾矿库放射性核素污染监测定位;最后,在不同的锚节点数、通信半径以及噪声标准差条件下,对比麻雀搜索优化定位算法(SSOLA)与加权质心定位算法(WCLA)、接收信号强度指示差定位算法(RSSID)、麻雀搜索定位算法(SSA)、粒子群定位算法(PSO)以及樽海鞘群定位算法(SAP)的性能。结果表明:SSOLA与其余5种算法相比定位误差平均下降41.9%、45.2%、26.8%、39.9%和36.9%,定位精度更高,收敛速度更快。In order to improve the positioning accuracy and convergence speed of localization algorithm for uranium tailings reservoir in WSN,the optimized sparrow search algorithm(SSA)was used to improve the localization algorithm based on signal strength indication(RSSI).Firstly,the chaotic mapping and elite opposition based learning were introduced to initialize the sparrow population,enrich the diversity of the population,and improve the global optimization ability of the algorithm.Secondly,the levy flight strategy was used to improve the searcher's position update method to avoid falling into local optimum.Then,the optimized SSA was used to replace the least square method to locate unknown nodes,and the localization algorithm was applied to the monitoring and positioning of radionuclide pollution in uranium tailings reservoir.Finally,under the conditions of different number of anchor nodes,communication radius and noise standard deviation,the sparrow search optimization localization algorithm(SSOLA)was compared with the weighted centroid localization algorithm(WCLA),received signal strength indicator difference localization algorithm(RSSID),sparrow search localization algorithm(SSA),particle swarm localization algorithm(PSO)and the salp swarm localization algorithm(SAP).The results show that compared with the other five algorithms,the positioning error of SSOLA has decreased by 41.9%,45.2%,26.8%,39.9%and 36.9%on average,with higher positioning accuracy and faster convergence speed.

关 键 词：麻雀搜索优化(SSO) 铀尾矿库 无线传感器网络(WSN) 定位算法 混沌映射 精英反向学习 莱维飞行策略 

分 类 号：X936[环境科学与工程—安全科学]