A 3-Tier Architecture for Network Latency Reduction in Healthcare Internet-of-Things Using Fog Computing and Machine Learning

Saurabh Shukla, M. Hassan, L. T. Jung, A. Awang, Muhammad Khalid Khan
{"title":"A 3-Tier Architecture for Network Latency Reduction in Healthcare Internet-of-Things Using Fog Computing and Machine Learning","authors":"Saurabh Shukla, M. Hassan, L. T. Jung, A. Awang, Muhammad Khalid Khan","doi":"10.1145/3316615.3318222","DOIUrl":null,"url":null,"abstract":"Healthcare Internet-of-things comprises a huge number of wearable sensors and interconnected computers. The high volume of IoT data is transacted over servers leading to servers overloading with high traffic causing network congestion. These cloud servers are typically for analyzing, retrieving and storing the large data generated from IoT devices. There exist challenges regarding sending real-time healthcare data from cloud servers to end-users. These challenges include the high computational latency, high communication latency, and high network latency. Due to these challenges, IoTs may not be able to send data in real-time to end-users. Fog nodes can be used to play a major role in reducing the high delay and high traffic. It can be a solution to increase system performance. In this paper, we proposed a 3-tier architecture, an analytical model for healthcare IoT using a hybrid approach consisting of fuzzy logic and reinforcement learning in a fog computing environment. The aim is to minimize network latency. The proposed model and 3-tier architecture are simulated using iFogSim simulator.","PeriodicalId":268392,"journal":{"name":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 8th International Conference on Software and Computer Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3316615.3318222","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15

Abstract

Healthcare Internet-of-things comprises a huge number of wearable sensors and interconnected computers. The high volume of IoT data is transacted over servers leading to servers overloading with high traffic causing network congestion. These cloud servers are typically for analyzing, retrieving and storing the large data generated from IoT devices. There exist challenges regarding sending real-time healthcare data from cloud servers to end-users. These challenges include the high computational latency, high communication latency, and high network latency. Due to these challenges, IoTs may not be able to send data in real-time to end-users. Fog nodes can be used to play a major role in reducing the high delay and high traffic. It can be a solution to increase system performance. In this paper, we proposed a 3-tier architecture, an analytical model for healthcare IoT using a hybrid approach consisting of fuzzy logic and reinforcement learning in a fog computing environment. The aim is to minimize network latency. The proposed model and 3-tier architecture are simulated using iFogSim simulator.
使用雾计算和机器学习减少医疗保健物联网网络延迟的三层体系结构
医疗物联网由大量可穿戴传感器和互联计算机组成。大量的物联网数据通过服务器进行处理,导致服务器过载,高流量导致网络拥塞。这些云服务器通常用于分析、检索和存储物联网设备生成的大数据。在从云服务器向最终用户发送实时医疗保健数据方面存在挑战。这些挑战包括高计算延迟、高通信延迟和高网络延迟。由于这些挑战,物联网可能无法实时向最终用户发送数据。雾节点可以在降低高时延和高流量方面发挥重要作用。它可以作为提高系统性能的解决方案。在本文中,我们提出了一个三层架构,这是一个在雾计算环境中使用模糊逻辑和强化学习混合方法的医疗物联网分析模型。其目的是最小化网络延迟。利用iFogSim模拟器对该模型和三层结构进行了仿真。
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