期刊
SENSORS
卷 21, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/s21186058
关键词
Internet of Vehicles; mobile edge computing; task offloading; Stackelberg game; reinforcement learning
资金
- National Natural Science Foundation of China [62071470, 61971421]
- Science and Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences [CAAS-ASTIP-2021-AII-01]
This study introduces a task offloading algorithm for the Internet of Vehicles, utilizing local-edge clouds and reinforcement learning to optimize computing efficiency. The algorithm increases task success probability and achieves a balance between various constraints on utility.
Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints.
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