Secure-fault-tolerant efficient industrial internet of healthcare things framework based on digital twin federated fog-cloud networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253152" target="_blank" >RIV/61989100:27240/23:10253152 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1319157823003014" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1319157823003014</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jksuci.2023.101747" target="_blank" >10.1016/j.jksuci.2023.101747</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Secure-fault-tolerant efficient industrial internet of healthcare things framework based on digital twin federated fog-cloud networks

Popis výsledku v původním jazyce

The Industrial Internet of Healthcare Things (IIoHT) is the emerging paradigm in digital healthcare. Context-aware healthcare sensors, local intelligent watches, healthcare devices, wireless communication technologies, fog, and cloud computing are all parts of the IIoHT used in healthcare. The ubiquitous healthcare services it provides to its users in practice. However, the current IIoHT healthcare frameworks have security and failure issues in mobile fog and cloud networks where they are spread out. This paper presents the secure, fault-tolerant IIoHT Framework based on digital twin (DT) federated learning-enabled fog-cloud models. The DT is an effective technology that makes virtual copies of servers at different locations. DT integrated with federated learning inside the fog and cloud environments, where the failure of tasks and execution improved for healthcare sensor data. The study aims to reduce processing time and the risk of task failure. The study presents the Secure and Fault-Tolerant Strategies (SFTS)-enabled IIoHT framework that optimizes wearable sensor data and executes it with the minimum offloading and processing delays. Simulation results show that the proposed work minimized the security risk by 40%, failure risk of tasks risk by 50%, and the training and testing time by 39% for sensor data during the execution of mobile fog cloud networks. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Název v anglickém jazyce

Secure-fault-tolerant efficient industrial internet of healthcare things framework based on digital twin federated fog-cloud networks

Popis výsledku anglicky

The Industrial Internet of Healthcare Things (IIoHT) is the emerging paradigm in digital healthcare. Context-aware healthcare sensors, local intelligent watches, healthcare devices, wireless communication technologies, fog, and cloud computing are all parts of the IIoHT used in healthcare. The ubiquitous healthcare services it provides to its users in practice. However, the current IIoHT healthcare frameworks have security and failure issues in mobile fog and cloud networks where they are spread out. This paper presents the secure, fault-tolerant IIoHT Framework based on digital twin (DT) federated learning-enabled fog-cloud models. The DT is an effective technology that makes virtual copies of servers at different locations. DT integrated with federated learning inside the fog and cloud environments, where the failure of tasks and execution improved for healthcare sensor data. The study aims to reduce processing time and the risk of task failure. The study presents the Secure and Fault-Tolerant Strategies (SFTS)-enabled IIoHT framework that optimizes wearable sensor data and executes it with the minimum offloading and processing delays. Simulation results show that the proposed work minimized the security risk by 40%, failure risk of tasks risk by 50%, and the training and testing time by 39% for sensor data during the execution of mobile fog cloud networks. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of King Saud University - Computer and Information Sciences

ISSN

1319-1578

e-ISSN

2213-1248

Svazek periodika

35

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

SA - Království Saúdská Arábie

Počet stran výsledku

22

Strana od-do

—

Kód UT WoS článku

001082356800001

EID výsledku v databázi Scopus

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