{"title":"Edge Computing Empowered Smart Healthcare: Monitoring and Diagnosis with Deep Learning Methods","authors":"","doi":"10.1007/s10723-023-09726-2","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Nowadays, data syncing before switchover and migration are two of the most pressing issues confronting cloud-based architecture. The requirement for a centrally managed IoT-based infrastructure has limited scalability due to security problems with cloud computing. The fundamental factor is that health systems, such as health monitoring, etc., demand computational operations on large amounts of data, which leads to the sensitivity of device latency emerging during these systems. Fog computing is a novel approach to increasing the effectiveness of cloud computing by allowing the use of necessary resources and close to end users. Existing fog computing approaches still have several drawbacks, including the tendency to either overestimate reaction time or consider result correctness, but managing both at once compromises system compatibility. To focus on deep learning algorithms and automated monitoring, FETCH is a proposed framework that connects with edge computing devices. It provides a constructive framework for real-life healthcare systems, such as those treating heart disease and other conditions. The suggested fog-enabled cloud computing system uses FogBus, which exhibits benefits in terms of power consumption, communication bandwidth, oscillation, delay, execution duration, and correctness.</p>","PeriodicalId":54817,"journal":{"name":"Journal of Grid Computing","volume":"2 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Grid Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10723-023-09726-2","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Nowadays, data syncing before switchover and migration are two of the most pressing issues confronting cloud-based architecture. The requirement for a centrally managed IoT-based infrastructure has limited scalability due to security problems with cloud computing. The fundamental factor is that health systems, such as health monitoring, etc., demand computational operations on large amounts of data, which leads to the sensitivity of device latency emerging during these systems. Fog computing is a novel approach to increasing the effectiveness of cloud computing by allowing the use of necessary resources and close to end users. Existing fog computing approaches still have several drawbacks, including the tendency to either overestimate reaction time or consider result correctness, but managing both at once compromises system compatibility. To focus on deep learning algorithms and automated monitoring, FETCH is a proposed framework that connects with edge computing devices. It provides a constructive framework for real-life healthcare systems, such as those treating heart disease and other conditions. The suggested fog-enabled cloud computing system uses FogBus, which exhibits benefits in terms of power consumption, communication bandwidth, oscillation, delay, execution duration, and correctness.
期刊介绍：
Grid Computing is an emerging technology that enables large-scale resource sharing and coordinated problem solving within distributed, often loosely coordinated groups-what are sometimes termed "virtual organizations. By providing scalable, secure, high-performance mechanisms for discovering and negotiating access to remote resources, Grid technologies promise to make it possible for scientific collaborations to share resources on an unprecedented scale, and for geographically distributed groups to work together in ways that were previously impossible. Similar technologies are being adopted within industry, where they serve as important building blocks for emerging service provider infrastructures.
Even though the advantages of this technology for classes of applications have been acknowledged, research in a variety of disciplines, including not only multiple domains of computer science (networking, middleware, programming, algorithms) but also application disciplines themselves, as well as such areas as sociology and economics, is needed to broaden the applicability and scope of the current body of knowledge.
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