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 ID | 34304 |
| Article Category | Article |
| Authors | Akram, Junaid, Tahir, Arsalan, Munawar, Hafiz Suliman, Akram, Awais, Kouzani, Abbas Z. and Mahmud, M. A. Parvez |
| Journal Title | Sensors |
| Journal Citation | 21 (23), pp. 1-22 |
| Article Number | 7846 |
| Number of Pages | 22 |
| Year | 2021 |
| Publisher | MDPI AG |
| Place of Publication | Switzerland |
| ISSN | 1424-8220 |
| 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. |
| Keywords | Binary particle swarm optimisation; Cloud computing; Fog computing; Makespan minimisation; Smart grid |
| Byline Affiliations | University of Sydney |
| Superior University, Pakistan | |
| National University of Sciences and Technology, Pakistan | |
| University of New South Wales | |
| COMSATS University Islamabad, Pakistan | |
| Deakin University | |
| Library Services |
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