无线传感器网络时间同步改进

Q. Gao, Baomin Xu
{"title":"无线传感器网络时间同步改进","authors":"Q. Gao, Baomin Xu","doi":"10.1109/SPCA.2006.297535","DOIUrl":null,"url":null,"abstract":"Wireless sensor networks (WSN) have emerged as an interesting and important research area in the last few years. Though the clock accuracy and precision requirements are often stricter than in traditional distributed systems, strict energy constraints limit the resources available to meet these goals. In this paper, we present a Bayesian approach to reduce time measurement uncertainty associated with message delivery delay. Our approach combines prior knowledge of time measurements from upstream nodes, measured time and measurement uncertainty of downstream nodes in order to obtain a more accurate time estimate. We verify the efficiency of this approach via simulations. For a 4 hop network, time synchronization precision with Bayesian estimation is about 4 times better than without it. We modeled a 100 hop network and show that time synchronization accuracy does not degrade significantly with the increase in number of hops being synchronized. Our approach is based on existing time synchronization algorithms and uses only local processing so that it does not add extra traffic","PeriodicalId":232800,"journal":{"name":"2006 First International Symposium on Pervasive Computing and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Time Synchronization Improvement for Wireless Sensor Networks\",\"authors\":\"Q. Gao, Baomin Xu\",\"doi\":\"10.1109/SPCA.2006.297535\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wireless sensor networks (WSN) have emerged as an interesting and important research area in the last few years. Though the clock accuracy and precision requirements are often stricter than in traditional distributed systems, strict energy constraints limit the resources available to meet these goals. In this paper, we present a Bayesian approach to reduce time measurement uncertainty associated with message delivery delay. Our approach combines prior knowledge of time measurements from upstream nodes, measured time and measurement uncertainty of downstream nodes in order to obtain a more accurate time estimate. We verify the efficiency of this approach via simulations. For a 4 hop network, time synchronization precision with Bayesian estimation is about 4 times better than without it. We modeled a 100 hop network and show that time synchronization accuracy does not degrade significantly with the increase in number of hops being synchronized. Our approach is based on existing time synchronization algorithms and uses only local processing so that it does not add extra traffic\",\"PeriodicalId\":232800,\"journal\":{\"name\":\"2006 First International Symposium on Pervasive Computing and Applications\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 First International Symposium on Pervasive Computing and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPCA.2006.297535\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 First International Symposium on Pervasive Computing and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPCA.2006.297535","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

摘要

近年来,无线传感器网络(WSN)成为一个有趣而重要的研究领域。尽管时钟精度和精度要求通常比传统的分布式系统更严格,但严格的能源约束限制了满足这些目标的可用资源。在本文中,我们提出了一种贝叶斯方法来减少与消息传递延迟相关的时间测量不确定性。我们的方法结合了上游节点的时间测量先验知识、下游节点的测量时间和测量不确定性,以获得更准确的时间估计。通过仿真验证了该方法的有效性。对于4跳网络,使用贝叶斯估计的时间同步精度比不使用贝叶斯估计的时间同步精度提高了4倍左右。我们模拟了一个100跳网络,并表明时间同步精度不会随着同步跳数的增加而显著降低。我们的方法基于现有的时间同步算法,并且只使用本地处理,因此不会增加额外的流量
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time Synchronization Improvement for Wireless Sensor Networks
Wireless sensor networks (WSN) have emerged as an interesting and important research area in the last few years. Though the clock accuracy and precision requirements are often stricter than in traditional distributed systems, strict energy constraints limit the resources available to meet these goals. In this paper, we present a Bayesian approach to reduce time measurement uncertainty associated with message delivery delay. Our approach combines prior knowledge of time measurements from upstream nodes, measured time and measurement uncertainty of downstream nodes in order to obtain a more accurate time estimate. We verify the efficiency of this approach via simulations. For a 4 hop network, time synchronization precision with Bayesian estimation is about 4 times better than without it. We modeled a 100 hop network and show that time synchronization accuracy does not degrade significantly with the increase in number of hops being synchronized. Our approach is based on existing time synchronization algorithms and uses only local processing so that it does not add extra traffic
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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:604180095
Book学术官方微信