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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2000315


Energy-efficient trajectory planning for a multi-UAV-assisted mobile edge computing system


Author(s):  Pei-qiu HUANG, Yong WANG, Kezhi WANG

Affiliation(s):  School of Automation, Central South University, Changsha 410083, China; more

Corresponding email(s):   pqhuang@csu.edu.cn, ywang@csu.edu.cn, kezhi.wang@northumbria.ac.uk

Key Words:  Multi-unmanned aerial vehicle, Mobile edge computing, Trajectory planning, Differential evolution, k-means clustering algorithm, Greedy method


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Pei-qiu HUANG, Yong WANG, Kezhi WANG. Energy-efficient trajectory planning for a multi-UAV-assisted mobile edge computing system[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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Abstract: 
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.

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