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Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring
In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537170/ https://www.ncbi.nlm.nih.gov/pubmed/34696135 http://dx.doi.org/10.3390/s21206923 |
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author | Mutlag, Ammar Awad Abd Ghani, Mohd Khanapi Mohammed, Mazin Abed Lakhan, Abdullah Mohd, Othman Abdulkareem, Karrar Hameed Garcia-Zapirain, Begonya |
author_facet | Mutlag, Ammar Awad Abd Ghani, Mohd Khanapi Mohammed, Mazin Abed Lakhan, Abdullah Mohd, Othman Abdulkareem, Karrar Hameed Garcia-Zapirain, Begonya |
author_sort | Mutlag, Ammar Awad |
collection | PubMed |
description | In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%. |
format | Online Article Text |
id | pubmed-8537170 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85371702021-10-24 Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring Mutlag, Ammar Awad Abd Ghani, Mohd Khanapi Mohammed, Mazin Abed Lakhan, Abdullah Mohd, Othman Abdulkareem, Karrar Hameed Garcia-Zapirain, Begonya Sensors (Basel) Article In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%. MDPI 2021-10-19 /pmc/articles/PMC8537170/ /pubmed/34696135 http://dx.doi.org/10.3390/s21206923 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mutlag, Ammar Awad Abd Ghani, Mohd Khanapi Mohammed, Mazin Abed Lakhan, Abdullah Mohd, Othman Abdulkareem, Karrar Hameed Garcia-Zapirain, Begonya Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring |
title | Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring |
title_full | Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring |
title_fullStr | Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring |
title_full_unstemmed | Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring |
title_short | Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring |
title_sort | multi-agent systems in fog–cloud computing for critical healthcare task management model (chtm) used for ecg monitoring |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537170/ https://www.ncbi.nlm.nih.gov/pubmed/34696135 http://dx.doi.org/10.3390/s21206923 |
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