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Dynamic deployment method based on double deep Q-network in UAV-assisted MEC systems

Zhang, Suqin ; Zhang, Lin ; Xu, Fei ; Cheng, Song ; Su, Weiya ; Wang, Sen

Journal of Cloud Computing, 2023-12, Vol.12 (1), p.130-16, Article 130 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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Título:
Dynamic deployment method based on double deep Q-network in UAV-assisted MEC systems
Autor: Zhang, Suqin ; Zhang, Lin ; Xu, Fei ; Cheng, Song ; Su, Weiya ; Wang, Sen
Assuntos: Algorithms ; Analysis ; Computer Communication Networks ; Computer Science ; Computer System Implementation ; Computer Systems Organization and Communication Networks ; Convergence ; Double deep Q-network ; Drone aircraft ; Dynamic deployment ; Edge computing ; Edge-cloud computing cooperation for task offloading in internet-of-things ; Energy consumption ; Energy limitation ; Geographical locations ; Information Systems Applications (incl.Internet) ; Machine learning ; Methods ; Mobile computing ; Mobile edge computing (MEC) ; Obstacle avoidance ; Optimization ; Questions and answers ; Software Engineering/Programming and Operating Systems ; Special Purpose and Application-Based Systems ; Terminals ; Unmanned aerial vehicle (UAV) ; Unmanned aerial vehicles
É parte de: Journal of Cloud Computing, 2023-12, Vol.12 (1), p.130-16, Article 130
Descrição: The unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) system leverages the high maneuverability of UAVs to provide efficient computing services to terminals. A dynamic deployment algorithm based on double deep Q-networks (DDQN) is suggested to address issues with energy limitation and obstacle avoidance when providing edge services to terminals by UAV. First, the energy consumption of the UAV and the fairness of the terminal’s geographic location are jointly optimized in the case of multiple obstacles and multiple terminals on the ground. And the UAV can avoid obstacles. Furthermore, a double deep Q-network was introduced to address the slow convergence and risk of falling into local optima during the optimization problem training process. Also included in the learning process was a pseudo count exploration strategy. Finally, the improved DDQN algorithm achieves faster convergence and a higher average system reward, according to experimental results. Regarding the fairness of geographic locations of terminals, the improved DDQN algorithm outperforms Q-learning, DQN, and DDQN algorithms by 50%, 20%, and 15.38%, respectively, and the stability of the improved algorithm is also validated.
Editor: Berlin/Heidelberg: Springer Berlin Heidelberg
Idioma: Inglês

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Dynamic deployment method based on double deep Q-network in UAV-assisted MEC systems

Zhang, Suqin ; Zhang, Lin ; Xu, Fei ; Cheng, Song ; Su, Weiya ; Wang, Sen

Berlin/Heidelberg: Springer Berlin Heidelberg

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