Completion time minimization for UAV-assisted mobile-edge computing systems

Xu, Y., Zhang, T.K., Loo, Jonathan ORCID: https://orcid.org/0000-0002-2197-8126, Yang, D.Y. and Xiao, L. (2021) Completion time minimization for UAV-assisted mobile-edge computing systems. IEEE Transactions on Vehicular Technology. ISSN 0018-9545

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Abstract

The explosive computation demands in the Internet of Things (IoT) have triggered the research interests on unmanned aerial vehicle (UAV) assisted mobile-edge computing (MEC) systems even though there are still many challenges, such as computing delay requirement, time slot partition, and trajectory design. This letter focuses on the computing delay issue in MEC systems assisted by multiple UAVs with the goal of task completion time minimization. In particular, both the partial offloading and binary offloading modes are considered by jointly optimizing time slot size, terminal devices (TDs) scheduling, computation resource allocation, and UAVs' trajectories. Particularly, an non-LoS channel model is adopted for UAV-ground communication. To handle the problem, we develop a joint optimization algorithm by invoking the concave-convex procedure method, Karush-Kuhn-Tucker conditions and penalized method. Numerical results show that the completion time can be significantly decreased by the proposed algorithm.

Item Type: Article
Additional Information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Task analysis, Unmanned aerial vehicles, Fading channels, Minimization, Resource management, Computational modeling, Approximation algorithms
Subjects: Computing > Systems > Distributed computing
Computing > Intelligent systems
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Depositing User: Jonathan Loo
Date Deposited: 24 Sep 2021 08:45
Last Modified: 24 Sep 2021 10:16
URI: http://repository.uwl.ac.uk/id/eprint/8265

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