# Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing
## 2 Literature Review
Their results show that the proposed algorithm achieves the efficient sharing of resources without requiring global information and assures the bounded divergence of the algorithm. Vehicular fog computing (VFC) is employed in [25], where the computation task is offloaded from a base station (BS) to underutilize the computational resources of vehicles in the vicinity. Major challenges in VFC, such as the absence of efficient incentives and mechanisms for task assignment, are presented. Firstly, a mechanism for efficient incentives is proposed, which is based on contract theoretical modeling, in which a contract is designed by examining the distinctive characteristics of every vehicle type to maximize the projected utility of the BS. The problem of task assignment is converted into a dual-sided matching problem between user equipment and vehicles, which is resolved by using a stable matching algorithm that is based on pricing. Their numerical results show a substantial performance improvement when using the proposed technique. Autonomous driving is a promising paradigm for enabling C-ITS. A significant amount of computation, as well as delay-intensive response, is needed to deal with autonomous driving. Therefore, in [26], VFC is applied to migrate computing resources from overcrowded BSs to nearby vehicles. Two-stage-based VFC architecture is suggested to address the challenges of server assignment and task processing tactics under the assumption of information asymmetry and uncertainty. Vehicular computing resources are managed according to contract theory and the efficient offloading of tasks is governed by learning-based matching. Their simulation results also exhibit improvements in performance, in terms of both resource management and offloading delay. VFC can also contribute to load issues in highly congested areas during peak times. Thus, vehicles can be assumed to be fog nodes, which are then used to assist in computing offloaded tasks. However, there are some concerns regarding the deployment of VFC, like the deficiency of incentives for the distribution of resources, the increased complexity of systems and collisions between offloading vehicles. An innovative contract-based procedure is proposed in [27] whereby joint resource contribution and utilization are achieved via deep reinforcement learning. The aim is to distribute resources while reducing the complexity of the system. Furthermore, to handle the collision problem in simultaneous offloading in multiple vehicle scenarios, a queuing model is also proposed. Their results show that the performance of both task offloading and resource allocation is significantly improved. Similar to the above model, in [28], the authors present the idea of vehicles enabled with computation capabilities, which ensures less latency and enhancements in overall system efficiency. A major issue faced in this scenario is how and which vehicle is to be selected for task offloading when taking into consideration the delay costs and resource allocation for the multi-vehicle model. A convex optimization problem is formulated for the consumption function of the offloading model, which is then subjected to inequality constraints. Further, this problem is solved using the Lagrange dual approach and, in the end, a low-complexity algorithm is designed to find the optimized values of offloading ratios, the selection of computing vehicles and the consumption of the system. Vehicular fog and cloud computing (VFCC) systems may consume large computing power for processing numerous computation-intensive and delay-sensitive tasks. To solve this problem, in [29], an optimal offloading scheme is proposed that takes into account the departure of occupied vehicles. Task offloading is formulated as a semi-Markov decision process (SMDP) and a value iteration algorithm is used to maximize the utility of the system. Compared to the greedy scheme, the proposed algorithm achieves higher utility. In [30], the authors propose a task offloading technique for vehicular networks.