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Title	A Cybertwin-Driven Intelligent Offloading Method For Iov Applications Using Drl In Smart Cities
ID_Doc	1212
Authors	Liu P.; Peng K.; Zhao B.
Year	2022
Published	Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022
DOI	http://dx.doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927948
Abstract	Internet of Vehicle (IoV) is considered as the enabling platform to improve the traffic condition in cities. Meanwhile, a variety of promising applications such as self-driving and route planning are realized, which generates various latency-critic tasks. These latency-critic tasks present high demands on computing capacity, while resource-restricted vehicles could not satisfy those requirements. Mobile edge computing (MEC) is emerging as a potential solution to mitigate resource-limited vehicles from latency-critic tasks, which enables vehicles to offload tasks to nearby road-side units. Besides, cybertwin can abstract IoV system into a digital entity to facilitate obtaining information and management. However, it is still challenging to leverage cybertwin technology to optimize offloading policy. To this end, we design a deep reinforcement learning method to train the optimal decision-making agent by a cybertwin-driven IoV system, which aims to minimize the system cost under latency constraints. The experimental results illustrate that the agent trained by our proposed method outperforms the other methods. © 2022 IEEE.
Author Keywords	Computation Offloading; Cybertwin; Deep Reinforcement Learning; IoV; Latency-Critic Task; Smart Cities

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