PSO localization algorithm based on node density weighting and distance correction

FANG Wangsheng; WANG Zhou; WU Weiwei; HU Zhongdong

doi:10.13873/J.1000-9787(2020)07-0127-03

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PSO localization algorithm based on node density weighting and distance correction

更新时间：2025-09-11

- PSO localization algorithm based on node density weighting and distance correction
- Vol. 39, Issue 7, Pages: 127-129+133(2020)
- 作者机构：
  
  江西理工大学信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)07-0127-03
  CLC： TN929.5;TP18;TP212.9
- Published：2020
- 稿件说明：
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[1]方旺盛,汪洲,吴伟伟,胡中栋.节点密度加权及距离修正的粒子群优化定位算法[J].传感器与微系统,2020,39(07):127-129+133.

FANG Wangsheng, WANG Zhou, WU Weiwei, et al. PSO localization algorithm based on node density weighting and distance correction[J]. 2020, 39(7): 127-129+133.
[1]方旺盛,汪洲,吴伟伟,胡中栋.节点密度加权及距离修正的粒子群优化定位算法[J].传感器与微系统,2020,39(07):127-129+133. DOI： 10.13873/J.1000-9787(2020)07-0127-03.

FANG Wangsheng, WANG Zhou, WU Weiwei, et al. PSO localization algorithm based on node density weighting and distance correction[J]. 2020, 39(7): 127-129+133. DOI： 10.13873/J.1000-9787(2020)07-0127-03.

摘要

为了充分利用无线传感器网络（WSNs）的拓扑结构来提高节点定位精度

提出一种基于节点密度加权的RDD算法。网络先计算出所有节点的密度加权值

利用节点通信半径乘以节点密度因子求出邻居节点之间的距离;为了减小累积误差

节点之间为两跳时

选取节点之间共有节点数最多的路径作为最优路径并求出节点之间的距离;节点之间为多跳时

采用迭代累加的方式求出节点之间的距离

再利用信标节点对距离进行修正

采用改进的粒子群算法来优化定位。实验仿真表明:本文提出的算法相对于改进的DV-Hop算法以及改进的RND算法定位精度有一定的提高。

Abstract

In order to make full use of the topological structure of wireless sensor networks（WSNs） to improve the node localization precision. A regulated density distance（RDD） algorithm based on node density weighting is proposed. The density weighting of all the nodes in the network are given. The distance between adjacent nodes is calculated by multiplying communication radius of node by the node density factor. In order to reduce the cumulative error

when the nodes are two hops

the path with the largest number of co-nodes among the nodes is selected as the optimal path and the distance between the nodes is calculated. When the nodes are multi-hops

the distance between the nodes is obtained by means of iterative accumulation. The beacon nodes are used to correct the distance. Improved particle swarm optimization algorithm is used to optimize the localization. The experimental results show that the proposed algorithm improves the positioning precision compared with the improved DV-Hop algorithm and the improved RND algorithm.

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references

基于跳距和位置优化的改进DV-Hop定位算法 [J]. 苟平章,李凤珍,贾向东. 传感器与微系统 . 2018(11)

基于最优跳距和LevyPSO算法的DV＿Hop定位算法 [J]. 檀爽,毛永毅. 传感技术学报 . 2018(06)

无线传感器网络中改进的DV-Hop定位算法 [J]. 李凤超,高美凤. 传感器与微系统 . 2018(02)

一种基于误差距离加权与跳段算法选择的遗传优化DV-Hop定位算法 [J]. 程超,钱志鸿,付彩欣,刘晓慧. 电子与信息学报 . 2015(10)

基于节点密度及路径优化的无线传感器网络定位算法研究 [J]. 吴凡,彭力. 传感技术学报 . 2014(11)

基于粒子群优化的三维测向交叉定位算法 [J]. 张伟. 传感器与微系统 . 2014(05)

A novel range-free localization based on regulated neighborhood distance for wireless ad hoc and sensor networks [J] . Guang Wu,Shu Wang,Bang Wang,Yan Dong,Shu Yan. Computer Networks . 2012 (16)

Algorithm 97: Shortest path [J] . Robert W. Floyd. Communications of the ACM . 1962 (6)

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