Vehicular fog computing (VFC) can be considered as an important alternative to address the existing challenges in intelligent transportation systems (ITS). The main purpose of VFC is to perform computational tasks through various vehicles. At present, VFCs include powerful computing resources that bring the computational resources nearer to the requesting devices. This paper presents a new algorithm based on meta-heuristic optimization method for task scheduling problem in VFC. The task scheduling in VFC is formulated as a multi-objective optimization problem, which aims to reduce makespan and monetary cost. The proposed method utilizes the grey wolf optimization (GWO) and assigns the different priorities to static and dynamic fog nodes. Dynamic fog nodes represent the parked or moving vehicles and static fog nodes show the stationary servers. Afterwards, the tasks that require the most processing resources are chosen and allocated to fog nodes. The GWO-based method is extensively evaluated in more details. Furthermore, the effectiveness of various parameters in GWO algorithm is analyzed. We also assess the proposed algorithm on real application and random data. The outcomes of our experiments confirm that, in comparison to previous works, our algorithm is capable of offering the lowest monetary cost.

翻译：车载雾计算（VFC）可视为应对智能交通系统（ITS）现有挑战的重要替代方案。VFC的主要目标是通过各类车辆执行计算任务。当前，VFC系统包含强大的计算资源，使计算资源更靠近请求设备。本文针对VFC中的任务调度问题，提出一种基于元启发式优化方法的新算法。该任务调度问题被建模为多目标优化问题，旨在减少完工时间与经济成本。所提方法采用灰狼优化算法，并为静态与动态雾节点分配不同优先级。动态雾节点代表停放或行驶中的车辆，静态雾节点则指固定服务器。随后，选取需要最多处理资源的任务并将其分配至雾节点。本文对基于GWO的方法进行了详尽评估，并分析了GWO算法中各类参数的有效性。我们还在实际应用与随机数据上验证了所提算法。实验结果表明，与现有研究相比，本算法能够实现最低的经济成本。