research-article

Study QoS-aware Fog Computing for Disease Diagnosis and Prognosis

Authors:

YiLin WangAuthors Info & Claims

Mobile Networks and Applications, Volume 28, Issue 2

Pages 452 - 459

https://doi.org/10.1007/s11036-022-01957-z

Published: 12 April 2022 Publication History

Abstract

The development of medical sensors and the Internet of Things (IoT) offers many opportunities for research on disease diagnosis and prognosis in the electronic healthcare (eHealth) industry. IoT medical applications use wearable medical sensor devices that can be connected to the Internet for remote monitoring. However, cloud computing technology cannot meet the real-time and low-latency requirements of IoT applications in eHealth. As an intermediate layer between things and clouds, fog computing has features such as enhanced low latency, mobility, network bandwidth, security and privacy. Therefore, fog computing is very useful for the diagnosis and prognosis of diseases in the eHealth industry. In this paper, we undertake a comprehensive survey on fog computing used in eHealth. We summarize the main challenges in the eHealth industry and analyze the corresponding solutions proposed by the existing works.

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Cited By

Zamani MSharifian S(2024)Distributed edge to cloud ensemble deep learning architecture to diagnose Covid-19 from lung image in IoT based e-Health systemThe Journal of Supercomputing10.1007/s11227-024-06163-080:13(18492-18520)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06163-0
Said O(2023)A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease SymptomsMobile Networks and Applications10.1007/s11036-023-02111-z28:4(1402-1420)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s11036-023-02111-z

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Published In

cover image Mobile Networks and Applications

Mobile Networks and Applications Volume 28, Issue 2

Apr 2023

431 pages

ISSN:1383-469X

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 12 April 2022

Accepted: 22 March 2021

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Funding Sources

National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
the Postgraduate Research & Practice Innovation Program of Jiangsu Province
Centre of Excellence in Cognition and its Disorders, Australian Research Council
Priority Academic Program Development of Jiangsu Higher Education Institutions

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Cited By

Zamani MSharifian S(2024)Distributed edge to cloud ensemble deep learning architecture to diagnose Covid-19 from lung image in IoT based e-Health systemThe Journal of Supercomputing10.1007/s11227-024-06163-080:13(18492-18520)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06163-0
Said O(2023)A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease SymptomsMobile Networks and Applications10.1007/s11036-023-02111-z28:4(1402-1420)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s11036-023-02111-z

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