Joint computation and communication design for UAV-assisted mobile edge computing in IoT

Zhang, T.K., Xu, Y., Loo, Jonathan ORCID: https://orcid.org/0000-0002-2197-8126, Yang, D.C. and Lin, X. (2020) Joint computation and communication design for UAV-assisted mobile edge computing in IoT. IEEE Transactions on Industrial Informatics, 16 (8). pp. 5505-5516. ISSN 1551-3203

[thumbnail of Loo_etal_IEEE_ToII_2019_Joint_computation_and_communication_design_for_UAV-assisted_mobile_edge_computing_in_IoT.pdf]
Preview
PDF
Loo_etal_IEEE_ToII_2019_Joint_computation_and_communication_design_for_UAV-assisted_mobile_edge_computing_in_IoT.pdf - Accepted Version

Download (1MB) | Preview

Abstract

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with a MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things (IoT) in a finite period. In this paper, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point (AP) via UAV relaying. We propose an optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV flight energy by optimizing the bits allocation, time slot scheduling and power allocation as well as UAV trajectory design. As the formulated problem is nonconvex and difficult to find the optimal solution, we propose to solve the problem by two parts, and obtain the near optimal solution by the Lagrangian duality method and successive convex approximation (SCA) technique, respectively. By analysis, the proposed algorithm can be guaranteed to converge within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.

Item Type: Article
Identifier: 10.1109/TII.2019.2948406
Additional Information: This work was supported by National Natural Science Foundation of China under Grants 61971060 and 61703197. © 2019 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.
Keywords: Internet of Things, mobile edge computing, re-source allocation, trajectory optimization, UAV communication
Subjects: Computing > Intelligent systems
Related URLs:
Depositing User: Jonathan Loo
Date Deposited: 15 Nov 2019 17:04
Last Modified: 06 Feb 2024 16:01
URI: https://repository.uwl.ac.uk/id/eprint/6535

Downloads

Downloads per month over past year

Actions (login required)

View Item View Item

Menu