Designing of Communication Systems in Advanced Metering Infrastructure (AMI) using Wi-Fi Offloading Technology

M. Shahraeini, Zeinab Farmani
{"title":"Designing of Communication Systems in Advanced Metering Infrastructure (AMI) using Wi-Fi Offloading Technology","authors":"M. Shahraeini, Zeinab Farmani","doi":"10.1109/ICCKE48569.2019.8964924","DOIUrl":null,"url":null,"abstract":"Smart grid denotes creating a two-way communication infrastructure connected to the electrical infrastructure. One of the most important steps of establishing smart grids in distribution level is the implementation of Advanced Metering Infrastructure (AMI). A variety of methods have been proposed to create a suitable platform for AMI, one of the most widely used of which is the use of data services of cellular networks. On the other hand, data volume in cellular networks has risen sharply and the need for data traffic management is highly felt. The Wi-Fi Offloading technique is one of the optimal methods for data traffic management in cellular networks. This study is assigned to the design of a data offloading platform for AMI. The proposed problem has been implemented and solved in the form of an optimization problem with a genetic algorithm. The simulation results indicate that data offloading has the ability for 100% management of AMI traffic. The results also show that increasing the coverage range of Wi-Fi antenna has a better performance than increasing the number of Wi-Fi access points.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"418 1","pages":"60-66"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCKE48569.2019.8964924","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Smart grid denotes creating a two-way communication infrastructure connected to the electrical infrastructure. One of the most important steps of establishing smart grids in distribution level is the implementation of Advanced Metering Infrastructure (AMI). A variety of methods have been proposed to create a suitable platform for AMI, one of the most widely used of which is the use of data services of cellular networks. On the other hand, data volume in cellular networks has risen sharply and the need for data traffic management is highly felt. The Wi-Fi Offloading technique is one of the optimal methods for data traffic management in cellular networks. This study is assigned to the design of a data offloading platform for AMI. The proposed problem has been implemented and solved in the form of an optimization problem with a genetic algorithm. The simulation results indicate that data offloading has the ability for 100% management of AMI traffic. The results also show that increasing the coverage range of Wi-Fi antenna has a better performance than increasing the number of Wi-Fi access points.
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
基于Wi-Fi卸载技术的高级计量基础设施通信系统设计
智能电网是指建立与电力基础设施相连的双向通信基础设施。在配电网层面建立智能电网的重要步骤之一是先进计量基础设施(AMI)的实施。人们提出了多种方法来创建适合AMI的平台,其中使用最广泛的方法之一是使用蜂窝网络的数据业务。另一方面,蜂窝网络的数据量急剧增加,对数据流量管理的需求日益突出。Wi-Fi卸载技术是蜂窝网络中数据流量管理的最佳方法之一。本研究的课题是AMI数据卸载平台的设计。该问题以遗传算法优化问题的形式实现并求解。仿真结果表明,数据分流具有100%管理AMI流量的能力。结果还表明,增加Wi-Fi天线的覆盖范围比增加Wi-Fi接入点的数量具有更好的性能。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Novel Parallel Jobs Scheduling Algorithm in The Cloud Computing Online QoS Multicast Routing in Multi-Channel Multi-Radio Wireless Mesh Networks using Network Coding Tasks Decomposition for Improvement of Genetic Network Programming Robust Real-time Magnetic-based Object Localization to Sensor’s Fault using Recurrent Neural Networks A Case Study for Presenting Bank Recommender Systems based on Bon Card Transaction Data
×
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