基于连通概率感知的车联网资源优化技术研究

研究一种自由连通状态下基于车辆速度的多车道连通概率分析模型。该模型主要关注各个路段的连通概率,基于随机过程统计分析理论推导出多车道的道路连通概率。其次,针对单播或多播的端到端的消息分发场景,提出一种基于连通度概率感知的资源优化选择算法。该算法通过考虑车辆速度及方向特征,宏观层次上选择连通度概率高的路段作为数据包转发的路径;微观层次上针对每一路段进行基于邻居信息的多跳广播,快速地将数据包从路段入口传输到路段出口,提高数据分发效率。NS2仿真结果表明,提出的资源优化算法提高了分组转发的有效性,降低了车载网络的分组传输时延。

Research on resource optimization technique based on connectivity probability perception for internet of vehicle

This paper presents a multi lane connectivity probability analysis model based on vehicle speed under free connection state, which focuses on the connectivity probabilities for various road segments, and calculates multilane connectivity probability based on stochastic process statistical analysis theory. Aiming at the unicast/multicast end to end packet delivery scenario, a resource optimization selection algorithm based on connectivity probability perception is proposed. In the proposed algorithm, the effect of vehicle speed and direction is described as the stochastic process of probability prediction in vehicular network. The best road segment with high connectivity probability is selected to forward packet on a macro level. On the other hand, in order to improve the packet successful delivery percentage, the neighbor information based multi hop broadcasting is proceeded to quickly transmit packets from the selected segment to another one on a micro level. NS2 simulation results show that the proposed resource optimization algorithm improves the effectiveness of packet delivery and reduces the end to end delay of packet transmission in internet of vehicle.