Distributed data acquisition optimization algorithm for wireless sensor networks

Q4 Engineering
Youxian Zhang , Zhen Nie , Hongxu Zhang
{"title":"Distributed data acquisition optimization algorithm for wireless sensor networks","authors":"Youxian Zhang ,&nbsp;Zhen Nie ,&nbsp;Hongxu Zhang","doi":"10.1016/j.measen.2025.101883","DOIUrl":null,"url":null,"abstract":"<div><div>With the rapid development of applications such as the Internet of Things and intelligent transportation, wireless sensor networks play an important role in data collection and environmental monitoring. However, wireless sensor networks face low efficiency and high energy consumption in distributed data collection and node configuration. In this context, a sensor node configuration optimization algorithm based on an improved sparrow search algorithm by introducing reverse elite selection, dynamic perturbation, and dynamic warning update strategies is proposed. Secondly, a virtual grid partitioning strategy is designed, and a distributed data collection and transmission optimization algorithm is proposed. The node configuration algorithm achieved the most uniform distribution of nodes in simulation testing and almost achieved complete region coverage. Under 30 % node failure, its network coverage rate was 83.5 %. When the packet size was 1000 kb, the data transmission rate and average communication delay of the data collection algorithm were 4.2 Mbps and 42 ms, respectively. Compared with existing algorithms, the proposed scheme performs well in coverage retention, energy consumption reduction, and fault recovery capability, and can meet the efficient and reliable distributed data collection needs of wireless sensor networks in complex environments.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"39 ","pages":"Article 101883"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement Sensors","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665917425000777","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

Abstract

With the rapid development of applications such as the Internet of Things and intelligent transportation, wireless sensor networks play an important role in data collection and environmental monitoring. However, wireless sensor networks face low efficiency and high energy consumption in distributed data collection and node configuration. In this context, a sensor node configuration optimization algorithm based on an improved sparrow search algorithm by introducing reverse elite selection, dynamic perturbation, and dynamic warning update strategies is proposed. Secondly, a virtual grid partitioning strategy is designed, and a distributed data collection and transmission optimization algorithm is proposed. The node configuration algorithm achieved the most uniform distribution of nodes in simulation testing and almost achieved complete region coverage. Under 30 % node failure, its network coverage rate was 83.5 %. When the packet size was 1000 kb, the data transmission rate and average communication delay of the data collection algorithm were 4.2 Mbps and 42 ms, respectively. Compared with existing algorithms, the proposed scheme performs well in coverage retention, energy consumption reduction, and fault recovery capability, and can meet the efficient and reliable distributed data collection needs of wireless sensor networks in complex environments.
无线传感器网络分布式数据采集优化算法
随着物联网、智能交通等应用的快速发展,无线传感器网络在数据采集、环境监测等方面发挥着重要作用。然而,无线传感器网络在分布式数据采集和节点配置方面存在效率低、能耗高的问题。在此背景下,提出了一种基于改进麻雀搜索算法的传感器节点配置优化算法,该算法引入了逆向精英选择、动态摄动和动态预警更新策略。其次,设计了虚拟网格分区策略,并提出了分布式数据采集与传输优化算法。节点配置算法在仿真测试中实现了节点分布最均匀,几乎实现了完全的区域覆盖。在30%节点故障情况下,其网络覆盖率为83.5%。当数据包大小为1000kb时,数据采集算法的数据传输速率为4.2 Mbps,平均通信时延为42 ms。与现有算法相比,该方案在覆盖保持、能耗降低、故障恢复能力等方面具有较好的性能,能够满足复杂环境下无线传感器网络高效、可靠的分布式数据采集需求。
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
Measurement Sensors
Measurement Sensors Engineering-Industrial and Manufacturing Engineering
CiteScore
3.10
自引率
0.00%
发文量
184
审稿时长
56 days
×
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