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Offloading algorithm in edge computing for Internet of Vehicles based on graph neural network and deep reinforcement learning
- Wang Qian, Li Mingjin, Meng Xianjing, Geng Leilei
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Journal of Shandong University(Engineering Science). 2026, 56(4):
17-26.
doi:10.6040/j.issn.1672-3961.0.2025.205
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To fully utilize the resources within the Internet of Vehicles system when applying edge computing, and reduce the delay, energy consumption and processing costs required for task processing, an offloading algorithm in edge computing for Internet of Vehicles based on graph neural network and deep reinforcement learning was proposed. The task offloading problem was modeled as a Markov decision process to minimize the average delay and energy consumption for task processing. By leveraging the global feature learning capability of graph neural network, the originally independent vehicle entities were integrated into a cohesive whole, enabling each vehicle to make rational offloading decisions. Simulation results showed that, compared with local computing, full offloading, random decision, and double deep Q-network, the proposed algorithm reduced average delay by approximately 88.9%, 49.9%, 27.6%, and 9.4%, respectively, and reduced average costs by approximately 74.5%, 46.1%, 26.3%, and 6.2%, respectively. Compared with full offloading, random decision, and double deep Q-network, the proposed algorithm reduced average energy consumption by approximately 43.6%, 25.4%, and 4.2%, respectively.
