Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Mutlag, Ammar Awad, Abd Ghani, Mohd Khanapi, Mohammed, Mazin Abed, Lakhan, Abdullah, Mohd, Othman, Abdulkareem, Karrar Hameed, Garcia-Zapirain, Begonya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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
_version_ 1784588185592922112
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
work_keys_str_mv AT mutlagammarawad multiagentsystemsinfogcloudcomputingforcriticalhealthcaretaskmanagementmodelchtmusedforecgmonitoring
AT abdghanimohdkhanapi multiagentsystemsinfogcloudcomputingforcriticalhealthcaretaskmanagementmodelchtmusedforecgmonitoring
AT mohammedmazinabed multiagentsystemsinfogcloudcomputingforcriticalhealthcaretaskmanagementmodelchtmusedforecgmonitoring
AT lakhanabdullah multiagentsystemsinfogcloudcomputingforcriticalhealthcaretaskmanagementmodelchtmusedforecgmonitoring
AT mohdothman multiagentsystemsinfogcloudcomputingforcriticalhealthcaretaskmanagementmodelchtmusedforecgmonitoring
AT abdulkareemkarrarhameed multiagentsystemsinfogcloudcomputingforcriticalhealthcaretaskmanagementmodelchtmusedforecgmonitoring
AT garciazapirainbegonya multiagentsystemsinfogcloudcomputingforcriticalhealthcaretaskmanagementmodelchtmusedforecgmonitoring