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:由于基于雾的车辆互联网(IoV)配备了丰富的观念、计算、通信和储存资源,它为散装数据处理提供了新的解决办法;然而,车辆机动性的影响对内容传播服务的内容时间安排和资源分配提出了挑战;在本文件中,我们建议用一个时间变化资源关系图,以模拟感知、计算、通信和储存资源在对IoV内容传播过程的多个快照中的相互交织的影响。 根据这个图表模型,内容传播过程以数学优化问题为模型,其中考虑了延迟、容忍性和延迟敏感服务的服务质量。由于NP的完备性,优化问题被分解成一个联合链接和子频道调度子问题,以及一个联合动力和流量控制子问题。随后,建议为联合链接和子频道调度子问题列表程序提出一个连锁的低复杂性列表算法。此外,为权力和流动控制子问题开发了一个强有力的资源管理算法,其中考虑延迟的敏感服务和延迟敏感服务的服务质量。由于PNP的完整性,因此优化问题被分解成一个联合链接和子渠道的分流调度,子问题,并分解成一个联合的子问题。最后我们模拟了其他算法。</s>