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AuthorAbdellatif A.A.
AuthorMohamed A.
AuthorChiasserini C.F.
AuthorTlili M.
AuthorErbad A.
Available date2020-04-02T11:08:04Z
Publication Date2019
Publication NameIEEE Network
ResourceScopus
ISSN8908044
URIhttp://dx.doi.org/10.1109/MNET.2019.1800083
URIhttp://hdl.handle.net/10576/13767
AbstractImproving the efficiency of healthcare systems is a top national interest worldwide. However, the need to deliver scalable healthcare services to patients while reducing costs is a challenging issue. Among the most promising approaches for enabling smart healthcare (s-health) are edge-computing capabilities and next-generation wireless networking technologies that can provide real-time and cost-effective patient remote monitoring. In this article, we present our vision of exploiting MEC for s-health applications. We envision a MEC-based architecture and discuss the benefits that it can bring to realize in-network and context-aware processing so that the s-health requirements are met. We then present two main functionalities that can be implemented leveraging such an architecture to provide efficient data delivery, namely, multimodal data compression and edge-based feature extraction for event detection. The former allows efficient and low distortion compression, while the latter ensures high-reliability and fast response in case of emergency applications. Finally, we discuss the main challenges and opportunities that edge computing could provide and possible directions for future research.
SponsorThis work was made possible by GSRA grant #GSRA2-1-0609-14026 from the Qatar National Research Fund (a member of Qatar Foundation). The work of Amr Mohamed and Alaa Awad Abdellatif is partially supported by NPRP grant # 8-408-2-172. The findings achieved herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectsmart health
TitleEdge computing for smart health: Context-aware approaches, opportunities, and challenges
TypeArticle
Pagination196-203
Issue Number3
Volume Number33


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