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Vehicle fog computing (VFC) is proposed as a solution that can significantly reduce the task processing overload of base station during the peak time, where the vehicle as a fog node contributes idle computing resource for task processing. However, there are still many challenges in the deployment of VFC, such as the lack of specific incentives of resource contribution, high system complexity, and offloading collisions between vehicles when the vehicles are offloading tasks simultaneously. In this paper, we first propose a novel contract-based incentive mechanism that combines resource contribution and resource utilization. Based on that, we propose to use distributed deep reinforcement learning to allocate resources and reduce system complexity. Task offloading method based on the queuing model is also proposed to avoid decision collisions in multi-vehicles task offloading. Numerical experiment results demonstrate that our proposed scheme has achieved a significant improvement in task offloading and resource allocation performance. INDEX TERMS Vehicular fog computing, contract theory, deep reinforcement learning, resource allocation, task offloading.

Mobile edge computing (MEC) has been developed as a key technique to handle the explosive computation demands of vehicles. However, it is non-trivial to realize high-reliable and low-latency vehicular requirements among distributed and capacity-constrained MEC nodes. Besides, the dynamic and uncertain vehicular environments bring extra challenges to preserve the long-term satisfactory user experience. In this paper, an adaptive resource allocation approach is investigated to enhance the user experience in vehicular edge computing networks. Specifically, leveraging the idea of task scalability, a model for balancing computing quality and resource consumption is introduced to exploit the computational resources fully. Towards the goal of minimizing the long-term computing quality loss by specifying the needed resource and the expected quality of each running task, a mix-integer non-linear stochastic optimization problem is formulated to jointly optimize the allocation of radio and computing resources, as well as the task placement. Due to the unpredictable network states and the high computational complexity of the formulated problem, the long-term optimization problem is firstly decomposed into a series of one-slot problems, and then, an iterative algorithm is provided to derive a computation efficient solution. Finally, both rigorous theoretical analysis and extensive trace-driven simulations validate the efficacy of our proposed approach. INDEX TERMS Vehicular edge computing, long-term user experience, computing quality optimization, adaptive resource allocation.

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In this paper, we study the prediction of traffic flow in the presence of missing information from data set. Specifically, we adopt three different patterns to model the missing data structure, and apply two types of approaches for the imputation. In consequence, a forecasting model via deep learning based methods is proposed to predict the traffic flow from the recovered data set. The experiments demonstrate the effectiveness of using deep learning based imputation in improving the accuracy of traffic flow prediction. Based on the experimental results, we conduct a thorough discussion on the appropriate methods to predict traffic flow under various missing data conditions, and thus shedding the light for a practical design. INDEX TERMS Data missing imputation, deep learning, traffic flow prediction.