Energy Optimization for Computing Reuse in Unmanned Aerial Vehicle-assisted Edge Computing Systems

LI Bin; CAI Haichen; ZHAO Chuanxin; WANG Junyi

doi:10.11999/JEIT231061

Volume 46 Issue 7

Jul. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2740-2747

LI Bin, CAI Haichen, ZHAO Chuanxin, WANG Junyi. Energy Optimization for Computing Reuse in Unmanned Aerial Vehicle-assisted Edge Computing Systems[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2740-2747. doi: 10.11999/JEIT231061

Citation:

LI Bin, CAI Haichen, ZHAO Chuanxin, WANG Junyi. Energy Optimization for Computing Reuse in Unmanned Aerial Vehicle-assisted Edge Computing Systems[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2740-2747. doi: 10.11999/JEIT231061

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Energy Optimization for Computing Reuse in Unmanned Aerial Vehicle-assisted Edge Computing Systems

doi: 10.11999/JEIT231061

1.
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Computer and Information, Anhui Normal University, Wuhu 241002, China
3.
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China

Funds: The National Natural Science Foundation of China (62101277, 62371149), The Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (CRKL230203)

Received Date: 2023-10-07
Rev Recd Date: 2024-01-19

Available Online: 2024-02-05

Publish Date: 2024-07-29

Abstract

Abstract

To address the high computational performance demands of delay-sensitive tasks in complex terrains, the collaborative computation offloading scheme for reusable tasks in mobile edge computing with the assistance of Unmanned Aerial Vehicle (UAV) is proposed. Firstly, the minimization of the average total energy consumption is formulated by jointly optimizing user offloading, user transmission power, server assignment on UAV, computation frequencies of users and UAV servers, as well as UAV flight trajectory, while meeting the latency constraints. Secondly, a deep reinforcement learning approach is employed to solve the optimization problem, and a Soft Actor-Critic (SAC) based optimization algorithm is introduced. The SAC algorithm utilizes a maximum entropy policy to encourage exploration that enhances the algorithm’s exploration capabilities and accelerates the training convergence speed. Simulation results demonstrate that the proposed SAC algorithm effectively reduces the average total energy consumption of the system while exhibiting good convergence.
- Unmanned Aerial Vehicle (UAV),
- Mobile edge computing,
- Reusable tasks,
- Resource allocation,
- Soft Actor-Critic (SAC) algorithm

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References(15)

References

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