Research on a Task Offloading Strategy for the Internet of Vehicles Based on Reinforcement Learning

文献类型： 外文期刊

第一作者： Xiao, Shuo

作者： Xiao, Shuo;Wang, Shengzhi;Wang, Tianyu;Zhuang, Jiayu;Liu, Jiajia;Zhuang, Jiayu;Liu, Jiajia

作者机构：

关键词： Internet of Vehicles; mobile edge computing; task offloading; Stackelberg game; reinforcement learning

期刊名称：SENSORS （ 影响因子：3.576； 五年影响因子：3.735 ）

ISSN：

年卷期： 2021 年 21 卷 18 期

页码：

收录情况： SCI

摘要： 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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