Energy-Efficient Trajectory Planning for a Multi-UAV-Assisted Mobile Edge Computing System

Huang, Pei-Qiu, Wang, Yong and Wang, Kezhi (2020) Energy-Efficient Trajectory Planning for a Multi-UAV-Assisted Mobile Edge Computing System. Frontiers of Information Technology and Electronic Engineering, 21 (12). pp. 1713-1725. ISSN 2095-9184

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This paper studies a mobile edge computing system assisted by multiple unmanned aerial vehicles (UAVs), where the UAVs act as edge servers to provide computing services for Internet of Things devices. Our goal is to minimize the energy consumption of this system by planning the trajectories of these UAVs. This problem is difficult to address because when planning the trajectories, we need to not only consider the order of stop points (SPs), but also their deployment (including the number and location) and the association between UAVs and SPs. To tackle this problem, we present an energy-efficient trajectory planning algorithm (called TPA), which comprises three phases. In the first phase, a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same time. Then, the second phase employs the k-means clustering algorithm to group the given SPs into a set of clusters, where the number of clusters is equal to that of UAVs and each cluster contains all SPs visited by the same UAV. Finally, in the third phase, to quickly generate the trajectories of UAVs, we propose a low-complexity greedy method to construct the order of SPs in each cluster. Compared with other algorithms, the effectiveness of TPA is verified on a set of instances at different scales.

Item Type: Article
Uncontrolled Keywords: Multi-unmanned aerial vehicle, Mobile edge computing, Trajectory planning, Differential evolution, k-means clustering algorithm, Greedy method
Subjects: G400 Computer Science
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 30 Nov 2020 15:27
Last Modified: 16 Oct 2021 03:30

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