UAV-enabled Mobile Edge Computing for Resource Allocation using Cooperative Evolutionary Computation

Goudarzi, Shidrokh ORCID:, Soleymani, Seyed Ahmad, Wang, Wenwu and Xiao, Pei (2023) UAV-enabled Mobile Edge Computing for Resource Allocation using Cooperative Evolutionary Computation. IEEE Transactions on Aerospace and Electronic Systems. pp. 1-14. ISSN 0018-9251 (In Press)

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Edge computing is a viable paradigm for supporting the Industrial Internet of Things deployment by shifting computationally demanding tasks from resource-constrained devices to powerful edge servers. In this study, mobile edge computing (MEC) services are provided for multiple ground mobile nodes (MNs) through a time-division multiple access protocol using the unmanned aerial vehicle (UAV)-enabled edge servers. Remotely controlled UAVs can serve as MEC servers due to their adaptability and flexibility.
However, the current MEC approaches have proven ineffective in situations where the number of MNs rapidly increases, or network resources are sparsely distributed. Furthermore, suitable accessibility across wireless networks via MNs with an acceptable quality of service is a fundamental problem for conventional UAV-assisted communications. To tackle this issue, we present an optimized computation resource allocation model using cooperative evolutionary computation to solve the joint optimization problem of queuebased computation offloading and adaptive computing resource allocation. The developed method ensures the task computation delay of all MNs within a time block, optimizes the sum of MN’s accessibility rates, and reduces the energy consumption of the UAV and MNs while meeting task computation restrictions. Moreover, we propose a multilayer data flow processing system to make full use of the computational capability across the system. The top layer of the system contains the cloud centre, the middle layer contains the UAV-assisted MEC (U-MEC) servers, and the bottom layer contains the mobile devices. Our numerical analysis and simulation results prove that the proposed scheme outperforms conventional techniques such as equal offloading time allocation and straight-line flight.

Item Type: Article
Identifier: 10.1109/TAES.2023.3251967
Keywords: Manganese; Servers; Task analysis; Resource management; Autonomous aerial vehicles; Computational modeling; Optimization; Mobile edge computing (MEC);queue-based computation offloading;resource allocation;unmanned aerial vehicle (UAV)
Subjects: Computing
Depositing User: Shidrokh Goudarzi
Date Deposited: 22 Sep 2023 10:18
Last Modified: 01 Jun 2024 08:17

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