# Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing
## 1 Introduction  
Vehicular networks are important components of future intelligent transportation systems (ITSs). These networks equip vehicles with wireless transmitters and receivers, thus enabling them to share traffic information with other vehicles and the cloud [1, 2]. As a result, city-level traffic information can be collected and used to manage congestion on the roads and implement safe driving practices [3, 4, 5, 6]. Due to the increasing number of ITS applications, the data shared among ITS entities (including vehicles and infrastructure nodes, known as roadside units) are growing at a rapid pace [7, 8, 9, 10]. Also, these ITS applications require the computation of several application-related tasks, such as decisions on particular driving actions using the current state of traffic in the vicinity. Thus, fog computing nodes, in the form of roadside units (RSUs), are needed to manage the data collection and computation of application-related tasks [11, 12, 13, 14]. Vehicles offload their tasks to nearby RSUs, which act as fog nodes and provide computation services [15, 16]. Task offloading in vehicular fog computing works in four steps. In the first step, the appropriate fog node for a given task is selected [17]. This decision is made based on several factors, such as the workloads of fog nodes, the channels between vehicles and fog nodes, etc. In the second step, the task is transmitted to the selected fog node using wireless communication technology, such as 6G [18, 19]. In the third step, the task is computed at the fog node. Finally, the last step involves transmitting the result of the task back to the vehicle [20, 21]. A major challenge in vehicular fog computing is the consideration of vehicle mobility and dynamic wireless channel quality for making task offloading decisions [22]. In addition, other factors, such as computing resource availability at fog nodes, the distances between vehicles and fog nodes, etc. also need attention. Moreover, the offloading ratio, which dictates the percentage of tasks that are computed locally by the vehicle itself, also needs to be decided. Neighborhood vehicles that have available computing resources can also share their resources with task-generating vehicles, thus computing the tasks even closer than fog RSUs. While fog RSUs have much higher computing capacities, they also have higher computing loads that are generated by several vehicles in their range. Similarly, there are scenarios where the channel quality between vehicles, fog RSUs and task-generating source vehicles is not good. Thus, transmitting tasks to fog RSUs may result in higher transmission delay and increased overall task delay. Thus, cooperative computing by neighborhood vehicles will play an important role in supporting fog RSUs in managing task computation within vehicular networks. In this paper, we propose a data-enabled task offloading technique for vehicular fog computing. The key contributions of the proposed work are as follows:  
* We consider a scenario in which vehicles use fog RSUs and other nearby vehicles to compute a task. A vehicle with a task to compute offloads some percentage of the task to a fog RSU, based on the channel quality indicator (CQI) of the link with the fog RSU. The remaining task is offloaded to a cooperative computing vehicle in its neighborhood. * We propose a cooperative computing vehicle selection algorithm, based on the metrics of link connectivity time and computing availability time. These metrics consider factors such as source vehicle speed, potential computing vehicles' speed, distance between vehicles, the computing loads of vehicles and channel quality between vehicles. * We propose an ANN-based prediction model to predict the values of the link connectivity time and computing availability time of potential cooperative computing vehicles.