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Enabling technologies for fog computing in healthcare IoT systems

机译:医疗保健物联网系统中雾计算的启用技术

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Context: A fog computing architecture that is geographically distributed and to which a variety of heterogeneous devices are ubiquitously connected at the end of a network in order to provide collaboratively variable and flexible communication, computation, and storage services. Fog computing has many advantages and it is suited for the applications whereby real-time, high response time, and low latency are of the utmost importance, especially healthcare applications. Objectives: The aim of this study was to present a systematic literature review of the technologies for fog computing in the healthcare IoT systems field and analyze the previous. Providing motivation, limitations faced by researchers, and suggestions proposed to analysts for improving this essential research field. Methods: The investigations were systematically performed on fog computing in the healthcare field by all studies; furthermore, the four databases Web of Science (WoS), ScienceDirect, IEEE Xplore Digital Library, and Scopus from 2007 to 2017 were used to analyze their architecture, applications, and performance evaluation. Results: A total of 99 articles were selected on fog computing in healthcare applications with deferent methods and techniques depending on our inclusion and exclusion criteria. The taxonomy results were divided into three major classes; frameworks and models, systems (implemented or architecture), review and survey. Discussion: Fog computing is considered suitable for the applications that require real-time, low latency, and high response time, especially in healthcare applications. All these studies demonstrate that resource sharing provides low latency, better scalability, distributed processing, better security, fault tolerance, and privacy in order to present better fog infrastructure. Learned lessons: numerous lessons related to fog computing. Fog computing without a doubt decreased latency in contrast to cloud computing. Researchers show that simulation and experimental proportions ensure substantial reductions of latency is provided. Which it is very important for healthcare IoT systems due to real-time requirements. Conclusion: Research domains on fog computing in healthcare applications differ, yet they are equally important for the most parts. We conclude that this review will help accentuating research capabilities and consequently expanding and making extra research domains. (C) 2018 Elsevier B.V. All rights reserved.
机译:背景信息:一种雾计算架构,该雾计算架构在地理上分布,并且各种异构设备在网络末端普遍连接到该雾计算架构,以提供协作可变且灵活的通信,计算和存储服务。雾计算具有许多优点,并且适合实时,高响应时间和低延迟至关重要的应用程序,尤其是医疗保健应用程序。目标:这项研究的目的是对医疗物联网系统领域的雾计算技术进行系统的文献综述,并对其进行分析。提供动机,研究人员面临的局限性以及向分析师提出的改善这一重要研究领域的建议。方法:所有研究均对医疗领域的雾计算系统地进行了调查。此外,使用2007年至2017年这四个数据库Web of Science(WoS),ScienceDirect,IEEE Xplore数字图书馆和Scopus来分析其架构,应用程序和性能评估。结果:根据我们的纳入和排除标准,总共选择了99篇有关医疗应用中雾计算的文章,采用的方法和技术不同。分类结果分为三个主要类别:框架和模型,系统(已实现或架构),审查和调查。讨论:雾计算被认为适用于需要实时,低延迟和高响应时间的应用程序,特别是在医疗保健应用程序中。所有这些研究表明,资源共享提供了低延迟,更好的可伸缩性,分布式处理,更好的安全性,容错性和隐私性,以提供更好的雾化基础架构。经验教训:与雾计算有关的许多课程。与云计算相比,雾计算无疑减少了延迟。研究人员表明,仿真和实验比例可确保显着减少延迟。由于实时需求,这对于医疗物联网系统非常重要。结论:医疗保健应用中雾计算的研究领域有所不同,但是对于大多数部分来说,它们同样重要。我们得出结论,这次审查将有助于增强研究能力,从而扩大和扩大研究领域。 (C)2018 Elsevier B.V.保留所有权利。

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