School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China.
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100080, China.
Sensors (Basel). 2021 Sep 9;21(18):6058. doi: 10.3390/s21186058.
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.
如今，车辆越来越多地与物联网连接，这使它们能够获得高质量的服务。然而，众多的车载应用和时变的网络状态使得车载终端难以实现高效的计算。因此，基于本地-边缘云的三级模型和强化学习，我们提出了一种面向车联网（IoV）的任务卸载算法。首先，我们建立了车辆之间的通信方法及其成本函数。此外，根据车辆的实时状态，我们分析了它们的计算需求和价格函数。最后，我们提出了一种基于多智能体强化学习的经验驱动的任务卸载策略。仿真结果表明，该算法提高了任务的成功率，并在各种约束条件下实现了任务车辆延迟、支出、任务车辆效用和服务车辆效用之间的平衡。
