Weighted centroid algorithm for WSNs based on multiple coverage under three-dimensional modeling of mine and RSSI

FANG Wang-sheng; LEI Gao-xiang; LI Ning

doi:10.13873/J.1000-9787(2016)05-0128-04

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Weighted centroid algorithm for WSNs based on multiple coverage under three-dimensional modeling of mine and RSSI

更新时间：2025-09-11

- Weighted centroid algorithm for WSNs based on multiple coverage under three-dimensional modeling of mine and RSSI
- Vol. 35, Issue 5, Pages: 128-131(2016)
- 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(2016)05-0128-04
  CLC： TN929.5;TP212.9
- Published：2016
- 稿件说明：
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[1]方旺盛,雷高祥,李宁.基于矿井三维WSNs多重覆盖和RSSI的加权质心算法[J].传感器与微系统,2016,35(05):128-131.

FANG Wang-sheng, LEI Gao-xiang, LI Ning. Weighted centroid algorithm for WSNs based on multiple coverage under three-dimensional modeling of mine and RSSI[J]. 2016, 35(5): 128-131.
[1]方旺盛,雷高祥,李宁.基于矿井三维WSNs多重覆盖和RSSI的加权质心算法[J].传感器与微系统,2016,35(05):128-131. DOI： 10.13873/J.1000-9787(2016)05-0128-04.

FANG Wang-sheng, LEI Gao-xiang, LI Ning. Weighted centroid algorithm for WSNs based on multiple coverage under three-dimensional modeling of mine and RSSI[J]. 2016, 35(5): 128-131. DOI： 10.13873/J.1000-9787(2016)05-0128-04.

摘要

针对无线传感器网络（WSNs）的矿井灾害无线监测信息系统

提出一种基于矿井三维建模WSNs多重覆盖和RSSI的加权质心定位算法。根据矿井巷道的几何结构特征

将矿井建模成规则的拱型模型。介绍了一种矿井巷道固定节点无缝覆盖部署模型

并运用几何学求出了满足多重覆盖要求时的节点部署间距。在此基础上

提出了一种基于RSSI比值的加权质心定位算法

利用节点RSSI值求出加权系数

再对质心坐标进行加权处理。仿真实验表明:改进的质心算法有效地减少了定位误差

定位精度优于原算法。

Abstract

A weighted centroid location algorithm based on multiple coverage under three-dimensional modeling of mine and RSSI is proposed for underground mine disaster monitoring system which based on WSNs. Model underground tunnel as an arched shape according to geometric structure property of mine channel. A deployment model for seamless coverage of fixed node is presented

meanwhile

distance of node deployment to meet requirement of nodes multiple coverage is further figured out. Besides

a weighted centroid location algorithm based on RSSI ratio is presented and use RSSI value to address weighting factor and weighting centroid coordinates.Simulation show that the improved centroid algorithm reduces positioning error and achieves better positioning precision.

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references

基于RSSI的WSNs加权质心定位算法的改进 [J]. 丁恩杰,乔欣,常飞,乔莉. 传感器与微系统 . 2013(07)

煤矿井下基于RSSI的加权质心定位算法 [J]. 韩东升,杨维,刘洋,张玉. 煤炭学报 . 2013(03)

基于网格扫描的实现目标点覆盖的确定性传感器节点部署方法 [J]. 郭秀明,赵春江,杨信廷,孙传恒,李明,李文勇,周超. 传感技术学报 . 2012(01)

基于WSNs中距离函数和指数函数的三维质心定位算法 [J]. 占宏,黎善斌,胥布工. 传感器与微系统 . 2011(05)

信标节点链式部署的井下无线传感器网络定位算法 [J]. 乔钢柱,曾建潮. 煤炭学报 . 2010(07)

一种基于RSSI校正的三角形质心定位算法 [J]. 吕振,谭鹏立. 传感器与微系统 . 2010(05)

矿井巷道无线传感器网络分层拓扑控制策略 [J]. 周公博,朱真才,陈光柱,周丽娟. 煤炭学报 . 2010(02)

基于ZigBee的RSSI测距研究 [J]. 章坚武,张璐,应瑛,高锋. 传感技术学报 . 2009(02)

A holistic approach to wireless sensor network routing in underground tunnel environments [J] . Di Wu,Lichun Bao,Renfa Li. Computer Communications . 2010 (13)

Self-deployment of sensors for maximized coverage in underwater acoustic sensor networks [J] . Kemal Akkaya,Andrew Newell. Computer Communications . 2009 (7)

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