摘要
为了提高无线传感器网络中非基于距离的定位算法的精度,提出了一种利用松鼠搜索算法(Squirrel Search Algorithm,SSA)和粒子群优化(Particle Swarm Optimization,PSO)算法协同优化的距离向量跳段(Distance Vector-Hop,DV-Hop)定位算法(SSA-PSO)。首先,研究了传统的非测距DV-Hop算法定位过程中的误差来源;其次,引入接收信号强度(Received Signal Strength Indicators,RSSI)和校正因子来量化最小跳跃次数,并校正平均跳跃距离;最后,在未知节点估计过程中,采用改进的SSA代替最小二乘法,结合PSO算法,在标准SSA中引入了帐篷混沌初始化策略、位置贪婪选择策略和高斯变分策略,以提高最优性能。仿真结果表明,在不同的通信半径、锚定节点数量和节点总数下,与DV-Hop、遗传算法(Genetic Algorithm,GA)、SSA和PSO算法相比,SSA-PSO算法具有更高的定位精度。
To enhance the accuracy of non-range-based positioning algorithms in wireless sensor networks,a DV-Hop(Distance Vector-Hop)positioning algorithm using co-optimization of SSA(Squirrel Search Algorithm)and PSO(Particle Swarm Optimization)algorithm called SSA-PSO is proposed.First,this paper investigates the sources of error in the positioning process of the conventional non-ranging DV-Hop algorithm.Then,it introduces RSSI(Received Signal Strength Indicators)and correction factors to quantify the minimum hop counts while optimizing average hop distances.Finally,in the unknown node estimation process,the improved SSA replaces the ordinary least squares with PSO integration.Standard SSA is enhanced through three strategies:tent chaos initialization,greedy position selection,and Gaussian variation optimization.Simulation results demonstrate that SSA-PSO achieves superior positioning accuracy compared to DV-Hop,GA(Genetic Algorithm),SSA,and PSO algorithms across various communication ranges,anchor node quantities,and total node counts.
作者
曹群丹
余修武
刘永
CAO Qundan;YU Xiuwu;LIU Yong(School of Resource Environment and Safety Engineering,University of South China,Hengyang Hunan 421001,China;College of Electrical Engineering,University of South China,Hengyang Hunan 421001,China)
出处
《通信技术》
2025年第7期719-726,共8页
Communications Technology
基金
湖南省自然科学基金项目(2024JJ5338)
国家自然科学基金项目(11875164)
湖南省重点研发计划项目(2018SK2055)。
