Full Text:   <2910>

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CLC number: TN929.5; TP301.6

On-line Access: 2020-12-10

Received: 2020-07-02

Revision Accepted: 2020-09-09

Crosschecked: 2020-10-29

Cited: 0

Clicked: 5184

Citations:  Bibtex RefMan EndNote GB/T7714


Pei-qiu Huang


Yong Wang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.12 P.1713-1725


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

Author(s):  Pei-qiu Huang, Yong Wang, Ke-zhi 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:  Multiple unmanned aerial vehicles, Mobile edge computing, Trajectory planning, Differential evolution, k-means clustering algorithm, Greedy method

Pei-qiu Huang, Yong Wang, Ke-zhi Wang. Energy-efficient trajectory planning for a multi-UAV-assisted mobile edge computing system[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1713-1725.

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A1 - Pei-qiu Huang
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A1 - Ke-zhi Wang
J0 - Frontiers of Information Technology & Electronic Engineering
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DOI - 10.1631/FITEE.2000315

We study 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 UAVs. This problem is difficult to address because when planning the trajectories, we need to consider not only the order of stop points (SPs), but also their deployment (including the number and locations) and the association between UAVs and SPs. To tackle this problem, we present an energy-efficient trajectory planning algorithm (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. In the second phase, the k-means clustering algorithm is employed 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. 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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