{"title":"基于深度学习SOM调度算法的WSN能量增强","authors":"S. S. Sivaraju, C. Kumar","doi":"10.36548/jitdw.2022.3.008","DOIUrl":null,"url":null,"abstract":"Energy efficiency is one of the primary requirements for designing a successful Wireless Sensor Network (WSN) model. The WSN systems are generally made with a group of nodes that are operated with a small size battery device. To improve the energy efficiency of such WSNs several methodologies like clustering approach, mobile node technique and optimal route planning designs were developed. Scheduling method is yet an efficient model that is widely used in WSN applications, that allows the nodes to be operated only for a certain prescribed time. The proposed work utilizes the Self Organizing Maps (SOM) approach for improving the performances of the scheduling algorithms to a certain limit. SOM is a kind of artificial neural network that analyzes the problem based on competitive learning rather than the backpropagation methods. The work compares the proposed algorithm with the traditional Ant Colony and Software Defined Network approaches, wherein the proposed approach has shown an improvement in terms of energy conservation and network lifetime.","PeriodicalId":74231,"journal":{"name":"Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Energy Enhancement of WSN with Deep Learning based SOM Scheduling Algorithm\",\"authors\":\"S. S. Sivaraju, C. Kumar\",\"doi\":\"10.36548/jitdw.2022.3.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Energy efficiency is one of the primary requirements for designing a successful Wireless Sensor Network (WSN) model. The WSN systems are generally made with a group of nodes that are operated with a small size battery device. To improve the energy efficiency of such WSNs several methodologies like clustering approach, mobile node technique and optimal route planning designs were developed. Scheduling method is yet an efficient model that is widely used in WSN applications, that allows the nodes to be operated only for a certain prescribed time. The proposed work utilizes the Self Organizing Maps (SOM) approach for improving the performances of the scheduling algorithms to a certain limit. SOM is a kind of artificial neural network that analyzes the problem based on competitive learning rather than the backpropagation methods. The work compares the proposed algorithm with the traditional Ant Colony and Software Defined Network approaches, wherein the proposed approach has shown an improvement in terms of energy conservation and network lifetime.\",\"PeriodicalId\":74231,\"journal\":{\"name\":\"Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.36548/jitdw.2022.3.008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36548/jitdw.2022.3.008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

能源效率是设计一个成功的无线传感器网络(WSN)模型的主要要求之一。无线传感器网络系统通常由一组节点组成,这些节点由一个小尺寸的电池设备操作。为了提高无线传感器网络的能量利用率,研究了聚类方法、移动节点技术和最优路由规划设计等方法。调度方法是目前广泛应用于无线传感器网络的一种有效模型,它允许节点只在规定的时间内运行。本文利用自组织映射(SOM)方法在一定程度上提高了调度算法的性能。SOM是一种基于竞争学习而不是反向传播方法来分析问题的人工神经网络。该工作将所提出的算法与传统的蚁群和软件定义网络方法进行了比较,其中所提出的方法在节能和网络寿命方面显示出改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Energy Enhancement of WSN with Deep Learning based SOM Scheduling Algorithm
Energy efficiency is one of the primary requirements for designing a successful Wireless Sensor Network (WSN) model. The WSN systems are generally made with a group of nodes that are operated with a small size battery device. To improve the energy efficiency of such WSNs several methodologies like clustering approach, mobile node technique and optimal route planning designs were developed. Scheduling method is yet an efficient model that is widely used in WSN applications, that allows the nodes to be operated only for a certain prescribed time. The proposed work utilizes the Self Organizing Maps (SOM) approach for improving the performances of the scheduling algorithms to a certain limit. SOM is a kind of artificial neural network that analyzes the problem based on competitive learning rather than the backpropagation methods. The work compares the proposed algorithm with the traditional Ant Colony and Software Defined Network approaches, wherein the proposed approach has shown an improvement in terms of energy conservation and network lifetime.
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
期刊最新文献
Detection of Fake Job Advertisements using Machine Learning algorithms Effective Approach for Early Detection of Diabetes by Logistic Regression through Risk Prediction Verification System for Handwritten Signatures with Modular Neural Networks Economic and competitive potential of lignin-based thermoplastics using a multicriteria decision-making method Development of reinforced paper and mitigation of the challenges of raw material availability by utilizing Areca nut leaf
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1