Tang, Qiang and Wen, Sihao and He, Shiming and Yang, Kun (2024) Multi-UAV-Assisted Offloading for Joint Optimization of Energy Consumption and Latency in Mobile Edge Computing. IEEE Systems Journal, 18 (2). pp. 1414-1425. DOI https://doi.org/10.1109/jsyst.2024.3395845
Tang, Qiang and Wen, Sihao and He, Shiming and Yang, Kun (2024) Multi-UAV-Assisted Offloading for Joint Optimization of Energy Consumption and Latency in Mobile Edge Computing. IEEE Systems Journal, 18 (2). pp. 1414-1425. DOI https://doi.org/10.1109/jsyst.2024.3395845
Tang, Qiang and Wen, Sihao and He, Shiming and Yang, Kun (2024) Multi-UAV-Assisted Offloading for Joint Optimization of Energy Consumption and Latency in Mobile Edge Computing. IEEE Systems Journal, 18 (2). pp. 1414-1425. DOI https://doi.org/10.1109/jsyst.2024.3395845
Abstract
To address the performance limitations caused by the insufficient computing capacity and energy of edge internet of things devices (IoTDs), we proposed a multi-unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) application framework in this article. In this framework, UAVs equipped with high-performance computing devices act as aerial servers deployed in the target area to support data offloading and task computing for IoTDs. We formulated an optimization problem to jointly optimize the connection scheduling, computing resource allocation, and UAVs' flying trajectories, considering the device offloading priority, to achieve a joint optimization of energy consumption and latency for all IoTDs during a given time period. Subsequently, to address this problem, we employed deep reinforcement learning for dynamic trajectory planning, supplemented by optimization theory and heuristic algorithm based on matching theory to assist in solving connection scheduling and computing resource allocation. To evaluate the performance of proposed algorithm, we compared it with deep deterministic policy gradient, particle swarm optimization, random moving, and local execution schemes. Simulation results demonstrated that the multi-UAV-assisted MEC significantly reduces the computing cost of IoTDs. Moreover, our proposed solution exhibited effectiveness in terms of convergence and optimization of computing costs compared to other benchmark schemes.
Item Type: | Article |
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Uncontrolled Keywords: | Computing resource allocation; connection scheduling; deep reinforcement learning (DRL); mobile edge computing (MEC); trajectory optimization; unmanned aerial vehicles (UAVs) |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 03 Jul 2024 13:30 |
Last Modified: | 03 Jul 2024 13:30 |
URI: | http://repository.essex.ac.uk/id/eprint/38701 |
Available files
Filename: Accepted_Manuscript.pdf