一种基于改进蜘蛛猴算法的无线传感器网络节点定位方法  

A Node Localization Method for Wireless Sensor Networks Based on Improved Spider Monkey Algorithm

作　　者：单好民[1] 蔺守河 SHAN Haomin;LIN Shouhe(College of Electronic and Communication Engineering,Zhejiang Technical College of Posts and Telecom,Shaoxing 312366,China;School of Mathematics and Information Science,North Minzu University,Yinchuan 750021,China)

机构地区：[1]浙江邮电职业技术学院电子与通信工程学院,绍兴312366 [2]北方民族大学数学与信息科学学院,银川750021

出　　处：《湖南工程学院学报（自然科学版）》2023年第3期51-57,共7页Journal of Hunan Institute of Engineering(Natural Science Edition)

基　　金：浙江省教育厅一般科研项目(Y202250907).

摘　　要：针对无线传感器网络中存在的节点定位误差大、精度低的问题,提出了一种基于改进的蜘蛛猴优化算法的节点定位策略.首先,阐述了DV-HOP算法的节点定位模型;其次,针对蜘蛛猴算法存在求解精度低、容易陷入局部最优、收敛速度慢等缺点,采用基于混沌的种群初始化策略提升种群多样性,在局部领导者的学习阶段和策略阶段使用加权思想;最后,通过仿真实验,与基本蜘蛛猴算法、蚁群算法和粒子群算法进行对比,本文算法在锚节点数量、未知节点数量和区域面积等方面具有明显优势.The node localization strategy based on the improved spider monkey optimization(ISMO)is pro-posed to address the problems of large node localization errors and low accuracy in wireless sensor networks.Firstly,the node localization model of DV-HOP algorithm is described.Secondly,to address the shortcomings of spider monkey algorithm such as low solution accuracy,easy to fall into local optimum and slow conver-gence,a chaos-based population initialization strategy is used to improve the population diversity,and the weighting idea is used in the learning phase and strategy phase of the local leader.Finally,the simulation experi-ments are compared with the basic spider monkey algorithm,ant colony algorithm and particle swarm algo-rithm,which has better results in terms of the number of anchor nodes,the number of unknown nodes and the area of the region.

关 键 词：无线传感器网络 蜘蛛猴算法 混沌 加权 节点定位 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]