Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring
Ammar Awad Mutlag,Mohd Khanapi Abd Ghani,Mazin Abed Mohammed,Abdullah Lakhan,Othman Mohd,Karrar Hameed Abdulkareem,Begonya Garcia-Zapirain +6 more
TLDR
In this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset, where a multi-agent system is proposed to provide the complete management of the network from the edge to the cloud.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%.read more
Citations
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A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System
TL;DR: An Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed and simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.
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Novel DERMA Fusion Technique for ECG Heartbeat Classification
Qurat-ul-ain Mastoi,Teh Ying Wah,Mazin Abed Mohammed,Uzair Iqbal,Seifedine Kadry,Arnab Majumdar,Orawit Thinnukool +6 more
TL;DR: This study proposed the fusion technique, dual event-related moving average (DERMA) with the fractional Fourier-transform algorithm (FrlFT) to identify the abnormal and normal morphological events of the ECG signals.
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Secure and failure hybrid delay enabled a lightweight RPC and SHDS schemes in Industry 4.0 aware IIoHT enabled fog computing
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TL;DR: In this paper, the authors proposed a secure hybrid delay scheme (SHDS) for Industrial Internet of Healthcare Things (IIT) enabled applications, which schedules all healthcare workloads under their deadlines.
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A Systematic Review and IoMT Based Big Data Framework for COVID-19 Prevention and Detection
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Evolutionary Trends in Progressive Cloud Computing based Healthcare: Ideas, Enablers, and Barriers
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References
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Enabling technologies for fog computing in healthcare IoT systems
Ammar Awad Mutlag,Mohd Khanapi Abd Ghani,N. Arunkumar,Mazin Abed Mohammed,Mazin Abed Mohammed,Othman Mohd +5 more
TL;DR: A systematic literature review of the technologies for fog computing in the healthcare IoT systems field and analyzing the previous is presented, providing motivation, limitations faced by researchers, and suggestions proposed to analysts for improving this essential research field.
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TL;DR: A novel framework called HealthFog is proposed for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis.
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Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory
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Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Nets
TL;DR: This paper proposes a resource allocation strategy for fog computing based on priced timed Petri nets (PTPNs), by which the user can choose the satisfying resources autonomously from a group of preallocated resources.
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Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods
Mazin Abed Mohammed,Karrar Hameed Abdulkareem,Alaa S. Al-Waisy,Salama A. Mostafa,Shumoos Al-Fahdawi,Ahmed M. Dinar,Wajdi Alhakami,Abdullah Baz,Mohammed Nasser Al-Mhiqani,Hosam Alhakami,Nureize Arbaiy,Mashael S. Maashi,Ammar Awad Mutlag,Begona Garcia-Zapirain,Isabel de la Torre Díez +14 more
TL;DR: The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS.
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