Cooperative Task Offloading in Cybertwin-Assisted Vehicular Edge Computing

• Published in: 2022 IEEE 20th International Conference on Embedded and Ubiquitous Computing (EUC) pp. 66 - 73
• Main Authors: Zhang, Enchao, Zhao, Liang, Lin, Na, Zhang, Weijun, Hawbani, Ammar, Min, Geyong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2022
• Subjects: Decision making; Deep Reinforcement Learning; Digital Twins; Generative Adversarial Network; Generative adversarial networks; Mobile Edge Computing; Real-time systems; Roads; Simulation; Stability analysis; Task Offloading; Ubiquitous computing; Vehicular Edge Computing
• DOI: 10.1109/EUC57774.2022.00020

Vehicular Edge Computing (VEC) is a computing paradigm that brings Mobile Edge Computing (MEC) to the road and vehicular scenarios by providing low-latency and high-efficiency computation services. One key technology of VEC is task offloading, which allows vehicles to send computation tasks to surrounding Roadside Units (RSUs) for execution, thereby reducing service delay. However, the existing task offloading schemes face the important challenges because the vehicles with time-varying trajectories and limited computing resources need to process massive data with high complexity and diversity. In this paper, we propose a Cooperative Task Qffloading Scheme (CTOS) based on Cybertwin-assisted VEC. Specially, a novel Cybertwin-assisted VEC network architecture is established by applying the combination of the Digital-Twins (DT) and the Generative Adversarial Network (GAN). With the powerful prediction capability of GAN, the data of DT is advanced with the physical entity, which is an effective assistant for task offloading. Then, we leverage the distributed Deep Reinforcement Learning (DRL) to make offloading decisions, which consider the limited resources of RSUs and the cooperation of vehicles. The simulation results demonstrate that the proposed scheme can achieve excellent performance in terms of system stability and efficiency.