An improved weighted centroid localisation algorithm for wireless sensor networks in coal mine underground

Q3 Engineering
Haibo Liu, Yujie Dong, Fuzhong Wang
{"title":"An improved weighted centroid localisation algorithm for wireless sensor networks in coal mine underground","authors":"Haibo Liu, Yujie Dong, Fuzhong Wang","doi":"10.1504/ijsn.2020.10028585","DOIUrl":null,"url":null,"abstract":"In view of the practical characteristics of coal mine underground working environment and the low positioning accuracy of existing algorithm, an improved weighted centroid localisation algorithm based on received signal strength indicator (RSSI) is proposed. Firstly, the environmental parameters of RSSI ranging are modified by the least square method to eliminate the influence of various interferences on the measured data. The exponential factor and the modified RSSI value are directly calculated to determine the coordinates of the unknown node. The exponential factor is optimised by an improved quantum particle swarm optimisation algorithm based on the criterion of minimum root mean square error. The simulation results show that the proposed algorithm can reduce the influence of complex environment factors in the positioning process and has the better positioning accuracy than the traditional method, which meets requirements of personnel location precision in underground long-distance roadway.","PeriodicalId":39544,"journal":{"name":"International Journal of Security and Networks","volume":"87 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Security and Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijsn.2020.10028585","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

Abstract

In view of the practical characteristics of coal mine underground working environment and the low positioning accuracy of existing algorithm, an improved weighted centroid localisation algorithm based on received signal strength indicator (RSSI) is proposed. Firstly, the environmental parameters of RSSI ranging are modified by the least square method to eliminate the influence of various interferences on the measured data. The exponential factor and the modified RSSI value are directly calculated to determine the coordinates of the unknown node. The exponential factor is optimised by an improved quantum particle swarm optimisation algorithm based on the criterion of minimum root mean square error. The simulation results show that the proposed algorithm can reduce the influence of complex environment factors in the positioning process and has the better positioning accuracy than the traditional method, which meets requirements of personnel location precision in underground long-distance roadway.
煤矿井下无线传感器网络的改进加权质心定位算法
针对煤矿井下工作环境的实际特点和现有算法定位精度较低的问题,提出了一种改进的基于接收信号强度指标(RSSI)的加权质心定位算法。首先,采用最小二乘法对RSSI测距的环境参数进行修正,消除各种干扰对测量数据的影响;直接计算指数因子和修正后的RSSI值,确定未知节点的坐标。采用基于最小均方根误差准则的改进量子粒子群优化算法对指数因子进行优化。仿真结果表明,该算法能够降低定位过程中复杂环境因素的影响,具有比传统方法更好的定位精度,满足地下长距离巷道人员定位精度的要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
International Journal of Security and Networks
International Journal of Security and Networks Engineering-Safety, Risk, Reliability and Quality
CiteScore
1.40
自引率
0.00%
发文量
20
期刊介绍: IJSN proposes and fosters discussion on and dissemination of network security related issues.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信