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An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization

Smart grids (SGs) enhance the effectiveness, reliability, resilience, and energy-efficient operation of electrical networks. Nonetheless, SGs suffer from big data transactions which limit their capabilities and can cause delays in the optimal operation and management tasks. Therefore, it is clear th...

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Autores principales: Alsokhiry, Fahad, Annuk, Andres, Mohamed, Mohamed A., Marinho, Manoel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9968009/
https://www.ncbi.nlm.nih.gov/pubmed/36850350
http://dx.doi.org/10.3390/s23041752
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author Alsokhiry, Fahad
Annuk, Andres
Mohamed, Mohamed A.
Marinho, Manoel
author_facet Alsokhiry, Fahad
Annuk, Andres
Mohamed, Mohamed A.
Marinho, Manoel
author_sort Alsokhiry, Fahad
collection PubMed
description Smart grids (SGs) enhance the effectiveness, reliability, resilience, and energy-efficient operation of electrical networks. Nonetheless, SGs suffer from big data transactions which limit their capabilities and can cause delays in the optimal operation and management tasks. Therefore, it is clear that a fast and reliable architecture is needed to make big data management in SGs more efficient. This paper assesses the optimal operation of the SGs using cloud computing (CC), fog computing, and resource allocation to enhance the management problem. Technically, big data management makes SG more efficient if cloud and fog computing (CFC) are integrated. The integration of fog computing (FC) with CC minimizes cloud burden and maximizes resource allocation. There are three key features for the proposed fog layer: awareness of position, short latency, and mobility. Moreover, a CFC-driven framework is proposed to manage data among different agents. In order to make the system more efficient, FC allocates virtual machines (VMs) according to load-balancing techniques. In addition, the present study proposes a hybrid gray wolf differential evolution optimization algorithm (HGWDE) that brings gray wolf optimization (GWO) and improved differential evolution (IDE) together. Simulation results conducted in MATLAB verify the efficiency of the suggested algorithm according to the high data transaction and computational time. According to the results, the response time of HGWDE is 54 ms, 82.1 ms, and 81.6 ms faster than particle swarm optimization (PSO), differential evolution (DE), and GWO. HGWDE’s processing time is 53 ms, 81.2 ms, and 80.6 ms faster than PSO, DE, and GWO. Although GWO is a bit more efficient than HGWDE, the difference is not very significant.
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spelling pubmed-99680092023-02-27 An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization Alsokhiry, Fahad Annuk, Andres Mohamed, Mohamed A. Marinho, Manoel Sensors (Basel) Article Smart grids (SGs) enhance the effectiveness, reliability, resilience, and energy-efficient operation of electrical networks. Nonetheless, SGs suffer from big data transactions which limit their capabilities and can cause delays in the optimal operation and management tasks. Therefore, it is clear that a fast and reliable architecture is needed to make big data management in SGs more efficient. This paper assesses the optimal operation of the SGs using cloud computing (CC), fog computing, and resource allocation to enhance the management problem. Technically, big data management makes SG more efficient if cloud and fog computing (CFC) are integrated. The integration of fog computing (FC) with CC minimizes cloud burden and maximizes resource allocation. There are three key features for the proposed fog layer: awareness of position, short latency, and mobility. Moreover, a CFC-driven framework is proposed to manage data among different agents. In order to make the system more efficient, FC allocates virtual machines (VMs) according to load-balancing techniques. In addition, the present study proposes a hybrid gray wolf differential evolution optimization algorithm (HGWDE) that brings gray wolf optimization (GWO) and improved differential evolution (IDE) together. Simulation results conducted in MATLAB verify the efficiency of the suggested algorithm according to the high data transaction and computational time. According to the results, the response time of HGWDE is 54 ms, 82.1 ms, and 81.6 ms faster than particle swarm optimization (PSO), differential evolution (DE), and GWO. HGWDE’s processing time is 53 ms, 81.2 ms, and 80.6 ms faster than PSO, DE, and GWO. Although GWO is a bit more efficient than HGWDE, the difference is not very significant. MDPI 2023-02-04 /pmc/articles/PMC9968009/ /pubmed/36850350 http://dx.doi.org/10.3390/s23041752 Text en © 2023 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
Alsokhiry, Fahad
Annuk, Andres
Mohamed, Mohamed A.
Marinho, Manoel
An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization
title An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization
title_full An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization
title_fullStr An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization
title_full_unstemmed An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization
title_short An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization
title_sort innovative cloud-fog-based smart grid scheme for efficient resource utilization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9968009/
https://www.ncbi.nlm.nih.gov/pubmed/36850350
http://dx.doi.org/10.3390/s23041752
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