Vehicular Fog Computing (VFC) has been envisioned as a potential fog computing paradigm which aims to offload delay sensitive tasks to mobile fog vehicles instead of remote cloud in order to facilitate computational demands of smart villages close to rural highways. There exists challenges related to task offloading in VFC that need to be addressed. Most often, Road Side Units (RSUs) deployed along rural highways are energy constrained and they need to provide energy efficient scheduling services for the allocation of tasks to fog vehicles. On the other hand, energy consumption optimization is challenging, since scheduling decision of local processing of tasks incur computation cost while the allocation of tasks to fog vehicles incurs communication cost. Although the task offloading to VFC reduces response latency, it leads to higher RSU energy consumption contributed by the communication of task data to fog vehicles. Therefore, this paper presents an energy efficient vehicle scheduling problem for offloading of tasks to mobile fog nodes subject to satisfy constraints of task deadline and resource availability. To resolve high dimensionality issue caused by increased number of vehicles in RSU coverage, we propose an on-policy reinforcement leaning based scheduling algorithm combined with fuzzy logic based greedy heuristic, named as Fuzzy Reinforcement Learning (FRL). This greedy heuristic not only accelerates learning process, but also improves long term reward when compared to Q-learning algorithm. Extensive experiments have been performed to evaluate the proposed algorithm and the simulation results show that the proposed FRL algorithm outperforms other scheduling algorithms such as First Come First Serve (FCFS), Rate Monotonic Scheduling (RMS), Fuzzy and Distributed Task Allocation with Distributed Process (DTA_DP).

车载雾计算 (VFC) 被设想为一种潜在的雾计算范式，旨在将延迟敏感任务卸载到移动雾车而不是远程云，以促进靠近农村高速公路的智慧村庄的计算需求。VFC 中存在与任务卸载相关的挑战需要解决。大多数情况下，沿农村公路部署的路侧单元 (RSU) 受到能源限制，它们需要提供节能调度服务，以将任务分配给雾车。另一方面，能耗优化具有挑战性，因为任务本地处理的调度决策会产生计算成本，而将任务分配给雾车会产生通信成本。虽然任务卸载到 VFC 减少了响应延迟，它会导致更高的 RSU 能源消耗，因为将任务数据传输到雾车。因此，本文提出了一种节能车辆调度问题，在满足任务期限和资源可用性的约束下，将任务卸载到移动雾节点。为了解决 RSU 覆盖范围内车辆数量增加导致的高维问题，我们提出了一种基于策略强化学习的调度算法，结合基于模糊逻辑的贪婪启发式算法，称为模糊强化学习（FRL）。与 Q-learning 算法相比，这种贪婪的启发式算法不仅可以加速学习过程，还可以提高长期奖励。