Distributed detection in Neural Network based multihop Wireless Sensor Network

Jabal Raval, B. Jagyasi
{"title":"Distributed detection in Neural Network based multihop Wireless Sensor Network","authors":"Jabal Raval, B. Jagyasi","doi":"10.1109/SAS.2014.6798918","DOIUrl":null,"url":null,"abstract":"In this paper, a Neural Network based data aggregation approach to detect the binary events in a multi-hop Wireless Sensor Network has been proposed. We envision every node in a network as a unit of neuron which gets trained by using the neural network based back propagation algorithm. As compared to the LMS based Adaptive Weighted Aggregation scheme for tree network, the proposed Neural Network based wireless sensor network approach leads to a significant improvement in detection accuracy without much energy losses due to communication and computation overhead. We also compare the detection accuracy of the proposed Neural Network based scheme with that of the non-adaptive Bayesian approach which requires apriori knowledge of the sensor's performance indices.","PeriodicalId":125872,"journal":{"name":"2014 IEEE Sensors Applications Symposium (SAS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Sensors Applications Symposium (SAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAS.2014.6798918","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

In this paper, a Neural Network based data aggregation approach to detect the binary events in a multi-hop Wireless Sensor Network has been proposed. We envision every node in a network as a unit of neuron which gets trained by using the neural network based back propagation algorithm. As compared to the LMS based Adaptive Weighted Aggregation scheme for tree network, the proposed Neural Network based wireless sensor network approach leads to a significant improvement in detection accuracy without much energy losses due to communication and computation overhead. We also compare the detection accuracy of the proposed Neural Network based scheme with that of the non-adaptive Bayesian approach which requires apriori knowledge of the sensor's performance indices.
基于神经网络的多跳无线传感器网络分布式检测
提出了一种基于神经网络的数据聚合方法来检测多跳无线传感器网络中的二进制事件。我们将网络中的每个节点都看作是一个神经元单元,通过基于神经网络的反向传播算法对其进行训练。与基于LMS的树状网络自适应加权聚合方案相比,本文提出的基于神经网络的无线传感器网络方法在不造成通信和计算开销的能量损失的情况下,显著提高了检测精度。我们还比较了所提出的基于神经网络的方案与需要先验了解传感器性能指标的非自适应贝叶斯方法的检测精度。
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
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:481959085
Book学术官方微信