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research-article

Transformative synergy: SSEHCET—bridging mobile edge computing and AI for enhanced eHealth security and efficiency

Authors:

Mamoona Humayun,

Amjad Alsirhani,

Faeiz Alserhani,

Momina Shaheen,

Ghadah AlwakidAuthors Info & Claims

Journal of Cloud Computing, Volume 13, Issue 1

https://doi.org/10.1186/s13677-024-00602-2

Published: 08 February 2024 Publication History

Abstract

Blockchain technologies (BCT) are utilized in healthcare to facilitate a smart and secure transmission of patient data. BCT solutions, however, are unable to store data produced by IoT devices in smart healthcare applications because these applications need a quick consensus process, meticulous key management, and enhanced eprivacy standards. In this work, a smart and secure eHealth framework SSEHCET (Smart and Secure EHealth Framework using Cutting-edge Technologies) is proposed that leverages the potentials of modern cutting-edge technologies (IoT, 5G, mobile edge computing, and BCT), which comprises six layers: 1) The sensing layer-WBAN consists of medical sensors that normally are on or within the bodies of patients and communicate data to smartphones. 2) The edge layer consists of elements that are near IoT devices to collect data. 3) The Communication layer leverages the potential of 5G technology to transmit patients' data between multiple layers efficiently. 4) The storage layer consists of cloud servers or other powerful computers. 5) Security layer, which uses BCT to transmit and store patients' data securely. 6) The healthcare community layer includes healthcare professionals and institutions. For the processing of medical data and to guarantee dependable, safe, and private communication, a Smart Agent (SA) program was duplicated on all layers. The SA leverages the potential of BCT to protect patients' privacy when outsourcing data. The contribution is substantiated through a meticulous evaluation, encompassing security, ease of use, user satisfaction, and SSEHCET structure. Results from an in-depth case study with a prominent healthcare provider underscore SSEHCET's exceptional performance, showcasing its pivotal role in advancing the security, usability, and user satisfaction paradigm in modern eHealth landscapes.

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Cited By

Cui MZhao SYao J(2024)Short-term forecasting of surface solar incident radiation on edge intelligence based on AttUNetJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00624-w13:1Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1186/s13677-024-00624-w

Index Terms

Transformative synergy: SSEHCET—bridging mobile edge computing and AI for enhanced eHealth security and efficiency
1. Applied computing
  1. Life and medical sciences
2. Security and privacy

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Journal of Cloud Computing: Advances, Systems and Applications

Journal of Cloud Computing: Advances, Systems and Applications Volume 13, Issue 1

Dec 2024

2705 pages

EISSN:2192-113X

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© The Author(s) 2024.

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Hindawi Limited

London, United Kingdom

Publication History

Published: 08 February 2024

Accepted: 25 January 2024

Received: 13 December 2023

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Deanship of Scientific Research at Jouf University

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Cited By

Cui MZhao SYao J(2024)Short-term forecasting of surface solar incident radiation on edge intelligence based on AttUNetJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00624-w13:1Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1186/s13677-024-00624-w

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