摘要
针对距离矢量跳(DV-Hop)定位模型定位精度低、优化策略场景依赖性强的问题,提出一种基于函数分析和模拟定参的改进DV-Hop模型——函数修正距离矢量跳(FuncDV-Hop)定位模型。首先,分析DV-Hop模型的平均跳距、距离估计和最小二乘法中的误差原因,引入待定系数优化、阶跃函数分段实验、带等效点的权重函数策略和极大似然估计修正;其次,考虑多应用场景,用控制变量法,分别将总节点数、信标节点比例、通信半径、信标节点数和待测节点数作为变量,设计对照实验;最后,进行仿真定参和整合优化测试两阶段实验,最终的改进策略较原DV-Hop模型的定位精度提高了23.70%~75.76%,平均优化率57.23%。实验结果表明,FuncDV-Hop模型的优化率最高达到了50.73%,与基于遗传算法和神经动力学改进的DV-Hop模型相比,FuncDV-Hop模型的优化率提升了0.55%~18.77%。所提模型不引入其他参量,不增加无线传感器网络(WSN)的协议开销,且有效提高定位精度。
Considering the low positioning accuracy and strong scene dependence of optimization strategy in the Distance Vector Hop(DV-Hop)localization model,an improved DV-Hop model,Function correction Distance Vector Hop(FuncDV-Hop)based on function analysis and determining coefficients by simulation was presented.First,the average hop distance,distance estimation,and least square error in the DV-Hop model were analyzed.The following concepts were introduced:undetermined coefficient optimization,step function segmentation experiment,weight function approach using equivalent points,and modified maximum likelihood estimation.Then,in order to design control trials,the number of nodes,the proportion of beacon nodes,the communication radius,the number of beacon nodes,and the number of unknown nodes were all designed for multi-scenario comparison experiments by using the control variable technique.Finally,the experiment was split into two phases:determining coefficients by simulation and integrated optimization testing.Compared with the original DV-Hop model,the positioning accuracy of the final improved strategy is improved by 23.70%-75.76%,and the average optimization rate is 57.23%.The experimental results show that,the optimization rate of FuncDV-Hop model is up to 50.73%,compared with the DV-Hop model based on genetic algorithm and neurodynamic improvement,the positioning accuracy of FuncDV-Hop model is increased by 0.55%-18.77%.The proposed model does not introduce other parameters,does not increase the protocol overhead of Wireless Sensor Networks(WSN),and effectively improves the positioning accuracy.
作者
沈涵
王中生
周舟
王长元
SHEN Han;WANG Zhongsheng;ZHOU Zhou;WANG Changyuan(State and Provincial Joint Engineering Lab.of Advanced Network,Monitoring and Control(Xi’an Technological University),Xi’an Shaanxi 710021,China;School of Computer Science and Engineering,Xi’an Technological University,Xi’an Shaanxi 710021,China;School of Computer Science and Engineering,Changsha University,Changsha Hunan 410022,China)
出处
《计算机应用》
CSCD
北大核心
2024年第4期1219-1226,共8页
journal of Computer Applications
基金
国家自然科学基金资助项目(52072293)。
关键词
无线传感器网络
距离矢量跳定位模型
控制变量法
待定系数法
等效权重
极大似然估计
Wireless Sensor Network(WSN)
Distance Vector Hop(DV-Hop)localization model
control variate method
undetermined coefficient method
equivalent weight
maximum likelihood estimation
作者简介
沈涵(1996-),女,浙江杭州人,硕士研究生,主要研究方向:无线传感器网络、机器学习、群速度调控;通信作者:王中生(1966-),男,河南林州人,教授,硕士,CCF会员,主要研究方向:未来网络技术及应用、物联网工程,电子邮箱wzhsh1681@163.com;周舟(1983-),男,湖南长沙人,副教授,博士,CCF会员,主要研究方向:云计算、边缘计算、并行与分布式计算;王长元(1963-),男,陕西宝鸡人,教授,博士生导师,博士,主要研究方向:人工智能、计算机视觉、软件工程、人机环境交互技术。
