{"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":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10723-023-09726-2","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","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.
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