As the fog-based internet of vehicles (IoV) is equipped with rich perception, computing, communication and storage resources, it provides a new solution for the bulk data processing. However, the impact caused by the mobility of vehicles brings a challenge to the content scheduling and resource allocation of content dissemination service. In this paper, we propose a time-varying resource relationship graph to model the intertwined impact of the perception, computation, communication and storage resources across multiple snapshots on the content dissemination process of IoV. Based on this graph model, the content dissemination process is modeled as a mathematical optimization problem, where the quality of service of both delay tolerant and delay sensitive services are considered. Owing to its NP-completeness, the optimization problem is decomposed into a joint link and subchannel scheduling subproblem and as well a joint power and flow control subproblem. Then, a cascaded low complexity scheduling algorithm is proposed for the joint link and subchannel scheduling subproblem. Moreover, a robust resource management algorithm is developed for the power and flow control subproblem, where the channel uncertainties in future snapshots are fully considered in the algorithm. Finally, we conduct simulations to show that the effectiveness of the proposed approaches outperforms other state-of-art approaches.

翻译：随着基于雾计算的车辆互联网（IoV）配备了丰富的感知、计算、通信和存储资源，它为海量数据处理提供了新的解决方案。然而，车辆移动性带来的影响对内容分发服务中的内容调度和资源分配提出了挑战。本文提出一种时变资源关系图，用于建模感知、计算、通信和存储资源在多个快照间对IoV内容分发过程的交织影响。基于该图模型，将内容分发过程建模为一个数学优化问题，其中同时考虑了时延容忍服务和时延敏感服务的服务质量。鉴于该问题的NP完全性，将其分解为联合链路与子信道调度子问题以及联合功率与流控制子问题。针对联合链路与子信道调度子问题，提出一种级联低复杂度调度算法；针对功率与流控制子问题，开发了一种鲁棒资源管理算法，该算法充分考虑了未来快照中信道的不确定性。最后通过仿真验证，所提方法的有效性优于其他先进方法。