Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation

Article


Akram, Junaid, Tahir, Arsalan, Munawar, Hafiz Suliman, Akram, Awais, Kouzani, Abbas Z. and Mahmud, M. A. Parvez. 2021. "Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation." Sensors. 21 (23), pp. 1-22. https://doi.org/10.3390/s21237846
Article Title

Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation

ERA Journal ID34304
Article CategoryArticle
AuthorsAkram, Junaid, Tahir, Arsalan, Munawar, Hafiz Suliman, Akram, Awais, Kouzani, Abbas Z. and Mahmud, M. A. Parvez
Journal TitleSensors
Journal Citation21 (23), pp. 1-22
Article Number7846
Number of Pages22
Year2021
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN1424-8220
1424-8239
Digital Object Identifier (DOI)https://doi.org/10.3390/s21237846
Web Address (URL)https://www.mdpi.com/1424-8220/21/23/7846
Abstract

The smart grid (SG) is a contemporary electrical network that enhances the network’s performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To minimise the burden on the Cloud and optimise resource allocation, the concept of fog computing integration with cloud computing is presented. Fog has three essential functionalities: location awareness, low latency, and mobility. We offer a cloud and fog-based architecture for information management in this study. By allocating virtual machines using a load-balancing mechanism, fog computing makes the system more efficient (VMs). We proposed a novel approach based on binary particle swarm optimisation with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of the search space for finding the optimal solution. The BPSOSA technique is compared against the round robin, odds algorithm, and ant colony optimisation. In terms of response time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 53.99 ms, 82.08 ms, and 81.58 ms, respectively. In terms of processing time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 52.94 ms, 81.20 ms, and 80.56 ms, respectively. Compared to BPSOSA, ant colony optimisation has slightly better cost efficiency, however, the difference is insignificant.

KeywordsBinary particle swarm optimisation; Cloud computing; Fog computing; Makespan minimisation; Smart grid
Byline AffiliationsUniversity of Sydney
Superior University, Pakistan
National University of Sciences and Technology, Pakistan
University of New South Wales
COMSATS University Islamabad, Pakistan
Deakin University
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