Delay-Minimization Routing for Heterogeneous VANETs With Machine Learning Based Mobility Prediction
Delay-Minimization Routing for Heterogeneous VANETs With Machine Learning Based Mobility Prediction
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DOI：
10.1109/tvt.2019.2899627
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发表时间：
2019-04-01
影响因子：
6.8
通讯作者：
Shen, Xuemin
中科院分区：
文献类型：
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作者：
Tang, Yujie;Cheng, Nan;Shen, Xuemin
由于车辆的高机动性、动态的车辆间距和可变的车辆密度，在车载自组织网络(VANET)中建立和维护端到端连接是具有挑战性的。车辆的移动性预测可以解决上述挑战，因为它可以提供更好的路线规划，并在连续服务可用性方面提高VANET的整体性能。本文提出了一种基于移动性预测的车载自组网集中式布线方案，并辅以人工智能的软件定义网络控制器。具体地说，SDN控制器可以通过先进的人工神经网络技术执行准确的移动性预测。然后，在移动性预测的基础上，路侧单元(RSU)或基站(BS)可以估计网络拓扑频繁变化下每个车辆请求的成功传输概率和平均时延。该估计是基于车辆到达服从非齐次泊松过程的随机城市交通模型进行的。SDN控制器从被视为交换机的RSU和BS收集网络信息。基于全局网络信息，SDN控制器计算交换机(即BS和RSU)的最优路由路径。当源车辆和目的地车辆位于同一交换机的覆盖区域内时，将由RSU或BS独立地做出进一步的路由决策，以最大限度地减少车辆服务的总体延迟。RSU或BS通过车辆到车辆或车辆到基础设施的通信来调度车辆的请求，从源车辆到目的地车辆。仿真结果表明，我们提出的集中式路由方案在传输时延方面优于其他的集中式路由方案，并且对于不同的车速，我们提出的集中式路由方案的传输性能更健壮。
Establishing and maintaining end-to-end connections in a vehicular ad hoc network (VANET) is challenging due to the high vehicle mobility, dynamic inter-vehicle spacing, and variable vehicle density. Mobility prediction of vehicles can address the aforementioned challenge, since it can provide a better routing planning and improve overall VANET performance in terms of continuous service availability. In this paper, a centralized routing scheme with mobility prediction is proposed for VANET assisted by an artificial intelligence powered software-defined network (SDN) controller. Specifically, the SDN controller can perform accurate mobility prediction through an advanced artificial neural network technique. Then, based on the mobility prediction, the successful transmission probability and average delay of each vehicle's request under frequent network topology changes can be estimated by the roadside units (RSUs) or the base station (BS). The estimation is performed based on a stochastic urban traffic model in which the vehicle arrival follows a non-homogeneous Poisson process. The SDN controller gathers network information from RSUs and BS that are considered as the switches. Based on the global network information, the SDN controller computes optimal routing paths for switches (i.e., BS and RSU). While the source vehicle and destination vehicle are located in the coverage area of the same switch, further routing decision will be made by the RSUs or the BS independently to minimize the overall vehicular service delay. The RSUs or the BS schedule the requests of vehicles by either vehicle-to-vehicle or vehicle-to-infrastructure communication, from the source vehicle to the destination vehicle. Simulation results demonstrate that our proposed centralized routing scheme outperforms others in terms of transmission delay, and the transmission performance of our proposed routing scheme is more robust with varying vehicle velocity.