Next Article in Journal
Energy-Efficient Optimal Power Allocation for SWIPT Based IoT-Enabled Smart Meter
Previous Article in Journal
Enhanced Dynamic Spectrum Access in UAV Wireless Networks for Post-Disaster Area Surveillance System: A Multi-Player Multi-Armed Bandit Approach
Article

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

1
School of Computer Science, The University of Sydney, Camperdown, NSW 2006, Australia
2
Department of Computer Science, Superior University, Lahore 54000, Pakistan
3
Research Center for Modelling and Simulation, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
4
School of Built Environment, University of New South Wales, Kensington, NSW 2052, Australia
5
Department of Computer Science, COMSATS University Islamabad, Vehari 61100, Pakistan
6
School of Engineering, Deakin University, Burwood, VIC 3125, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Juan I. Guerrero and Antonio Martin-Montes
Sensors 2021, 21(23), 7846; https://doi.org/10.3390/s21237846
Received: 18 October 2021 / Revised: 16 November 2021 / Accepted: 18 November 2021 / Published: 25 November 2021
(This article belongs to the Special Issue Edge Computing Applied to the Industrial Environment)
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. View Full-Text
Keywords: smart grid; fog computing; binary particle swarm optimisation; cloud computing; makespan minimisation smart grid; fog computing; binary particle swarm optimisation; cloud computing; makespan minimisation
Show Figures

Figure 1

MDPI and ACS Style

Akram, J.; Tahir, A.; Munawar, H.S.; Akram, A.; Kouzani, A.Z.; Mahmud, M.A.P. Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation. Sensors 2021, 21, 7846. https://doi.org/10.3390/s21237846

AMA Style

Akram J, Tahir A, Munawar HS, Akram A, Kouzani AZ, Mahmud MAP. Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation. Sensors. 2021; 21(23):7846. https://doi.org/10.3390/s21237846

Chicago/Turabian Style

Akram, Junaid, Arsalan Tahir, Hafiz S. Munawar, Awais Akram, Abbas Z. Kouzani, and M A.P. Mahmud 2021. "Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation" Sensors 21, no. 23: 7846. https://doi.org/10.3390/s21237846

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop