Energy and task completion time minimization algorithm for UAVs-empowered MEC SYSTEM

• Published in: Sustainable computing informatics and systems Vol. 35; p. 100698
• Main Authors: Asim, Muhammad, Mashwani, Wali Khan, A. Abd El-Latif, Ahmed
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2022
• Subjects: Clustering algorithm; Differential evolution; Mobile edge computing; Tabu search algorithm; Unmanned aerial vehicle; Differential evolution; Unmanned aerial vehicle; Clustering algorithm; Mobile edge computing; Tabu search algorithm
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2022.100698

•We propose a new UAVs-empowered MEC system, where a number of UAVs are deployed to serve user equipments.•In order to reduce the weighted sum of the energy consumption and task completion time of the system, we optimize the trajectories of UAVs in a UAVs-empowered MEC system.•We propose an energy and task completion time minimization algorithm (ETCTMA) to solve the trajectory optimization problem in three phases.•To optimize the association between SPs and UAVs, we adopt an efficient low-complexity clustering algorithm.•Extensive experiments have been conducted on a set of seven instances ranging from 100 to 700 UEs. The simulation results reveal the superiority of the proposed algorithm. This paper presents an energy and task completion time minimization scheme for the unmanned aerial vehicles (UAVs)-empowered mobile edge computing (MEC) system, where several UAVs are deployed to serve large-scale users’ equipment (UEs). The aim is to minimize the weighted sum of energy consumption and task completion time of the system by planning the trajectories of UAVs. The problem is NP-hard, non-convex, non-linear, and mixed-decision variables. Therefore, it is very challenging to be solved by conventional optimization techniques. To handle this problem, this paper proposes an energy and task completion time minimization algorithm (ETCTMA) that solves the above problem in three steps. In the first step, the deployment updation of stop points (SPs) is handled by adopting a differential evolution algorithm with a variable population size. Then, in the second step, the association between SPs and UAVs is determined. Specifically, a clustering algorithm is proposed to associate SPs with UAVs. Finally, in the third step, a low-complexity tabu search algorithm is adopted to construct the trajectories of all UAVs. The performance of the proposed ETCTMA is tested on seven instances with up to 700 UEs. The results reveal that our proposed algorithm ETCTMA outperforms other variants in terms of minimizing the weighted sum of energy consumption and task completion time of the system.