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AuthorYang, Xin
AuthorNazir, Shah
AuthorKhan, Habib Ullah
AuthorShafiq, Muhammad
AuthorMukhtar, Neelam
Available date2022-12-28T06:02:45Z
Publication Date2021-01-23
Publication NameComplexity
Identifierhttp://dx.doi.org/10.1155/2021/6636898
CitationYang, X., Nazir, S., Khan, H. U., Shafiq, M., & Mukhtar, N. (2021). Parallel computing for efficient and intelligent industrial internet of health things: an overview. Complexity, 2021.
ISSN1076-2787
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85100298861&origin=inward
URIhttp://hdl.handle.net/10576/37693
AbstractInternet of Things (IoT) is expanding and evolves into all aspects of the society. Research and developments in the field of IoT have shown the possibility of producing huge volume of data and computation among different devices of the IoT. The data collected from IoT devices are transferred to a central server which can further be retrieved and accessed by the service providers for analyzing, processing, and using. Industrial Internet of Health Things (IIoHT) is the expansion of the Internet of Health Things (IoHT) which plays an important role in observing, consulting, monitoring, and treatment process of remote exchange data processes. The linkage of computation and interoperability are supported through various intelligent sensors, controllers, and actuators. The role of parallel computing for efficient and Intelligent Industrial Internet of Health Things is obvious to analyze and process different healthcare situations. A detailed overview of this existing literature is needed through which the research community will provide new solutions for efficient healthcare with the help of IoT based on parallel computing. Therefore, the current study presents a detailed overview of the existing literature for facilitating IIoHT.
Languageen
PublisherHindawi
SubjectIoT
Intelligent health care
Internet of Health Things
TitleParallel Computing for Efficient and Intelligent Industrial Internet of Health Things: An Overview
TypeArticle
Volume Number2021
ESSN1099-0526
dc.accessType Open Access


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