Efficient UAV-Based MEC Using GPU-Based PSO and Voronoi Diagrams

Mohamed Mousa; Mohamed Hussein

doi:10.32604/cmes.2022.020639

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ARTICLE

Efficient UAV-Based MEC Using GPU-Based PSO and Voronoi Diagrams

Mohamed H. Mousa^1,2,*, Mohamed K. Hussein²
1 Department of Information Technology, College of Computing & Information Technology at AlKamil, University of Jeddah, Jeddah, Saudi Arabia
2 Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt
* Corresponding Author: Mohamed H. Mousa. Email: mohamed
(This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)

Computer Modeling in Engineering & Sciences 2022, 133(2), 413-434. https://doi.org/10.32604/cmes.2022.020639

Received 04 December 2021; Accepted 18 February 2022; Issue published 21 July 2022

Abstract

Mobile-Edge Computing (MEC) displaces cloud services as closely as possible to the end user. This enables the edge servers to execute the offloaded tasks that are requested by the users, which in turn decreases the energy consumption and the turnaround time delay. However, as a result of a hostile environment or in catastrophic zones with no network, it could be difficult to deploy such edge servers. Unmanned Aerial Vehicles (UAVs) can be employed in such scenarios. The edge servers mounted on these UAVs assist with task offloading. For the majority of IoT applications, the execution times of tasks are often crucial. Therefore, UAVs tend to have a limited energy supply. This study presents an approach to offload IoT user applications based on the usage of Voronoi diagrams to determine task delays and cluster IoT devices dynamically as a first step. Second, the UAV flies over each cluster to perform the offloading process. In addition, we propose a Graphics Processing Unit (GPU)-based parallelization of particle swarm optimization to balance the cluster sizes and identify the shortest path along these clusters while minimizing the UAV flying time and energy consumption. Some evaluation results are given to demonstrate the effectiveness of the presented offloading strategy.

Keywords

Task offloading; mobile-edge computing; unmanned aerial vehicles; Internet of Things; voronoi diagrams; GPU; particle swarm optimization

Cite This Article

Mousa, M. H., Hussein, M. K. (2022). Efficient UAV-Based MEC Using GPU-Based PSO and Voronoi Diagrams. CMES-Computer Modeling in Engineering & Sciences, 133(2), 413–434.

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