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Article

Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring

by
Ammar Awad Mutlag
1,2,
Mohd Khanapi Abd Ghani
1,
Mazin Abed Mohammed
3,*,
Abdullah Lakhan
4,
Othman Mohd
1,
Karrar Hameed Abdulkareem
5 and
Begonya Garcia-Zapirain
6,*
1
Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia
2
Ministry of Education/General Directorate of Curricula, Pure Science Department, Baghdad 10065, Iraq
3
College of Computer Science and Information Technology, University of Anbar, 11, Ramadi 31001, Iraq
4
Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
5
College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
6
eVIDA Laboratory, University of Deusto, Avda/Universidades 24, 48007 Bilbao, Spain
*
Authors to whom correspondence should be addressed.
Sensors 2021, 21(20), 6923; https://doi.org/10.3390/s21206923
Submission received: 19 August 2021 / Revised: 7 October 2021 / Accepted: 9 October 2021 / Published: 19 October 2021
(This article belongs to the Special Issue Smart Sensors, Big Data Analytics and Modelling in Healthcare and Personalized Medical Applications)
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Abstract

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%.
Keywords: cloud computing; fog computing; scheduling; multi-agent system; balancing; prioritization; cardiology cloud computing; fog computing; scheduling; multi-agent system; balancing; prioritization; cardiology

Share and Cite

MDPI and ACS Style

Mutlag, A.A.; Ghani, M.K.A.; Mohammed, M.A.; Lakhan, A.; Mohd, O.; Abdulkareem, K.H.; Garcia-Zapirain, B. Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring. Sensors 2021, 21, 6923. https://doi.org/10.3390/s21206923

AMA Style

Mutlag AA, Ghani MKA, Mohammed MA, Lakhan A, Mohd O, Abdulkareem KH, Garcia-Zapirain B. Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring. Sensors. 2021; 21(20):6923. https://doi.org/10.3390/s21206923

Chicago/Turabian Style

Mutlag, Ammar Awad, Mohd Khanapi Abd Ghani, Mazin Abed Mohammed, Abdullah Lakhan, Othman Mohd, Karrar Hameed Abdulkareem, and Begonya Garcia-Zapirain. 2021. "Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring" Sensors 21, no. 20: 6923. https://doi.org/10.3390/s21206923

APA Style

Mutlag, A. A., Ghani, M. K. A., Mohammed, M. A., Lakhan, A., Mohd, O., Abdulkareem, K. H., & Garcia-Zapirain, B. (2021). Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring. Sensors, 21(20), 6923. https://doi.org/10.3390/s21206923

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