一种面向智慧交通的车联网网络流量估计方法

Method for estimation of vehicular network traffic for smart transportations

随着5G网络的快速部署,车联网、物联网、边缘计算等迅速发展,车联网络流量测量面对诸多挑战。研究了面向智慧城市的车联网流量估计问题,提出了一种基于软件定义网络的车联网流量估计方法。基于软件定义网络架构获得粗粒度的车联网络流量测量值,构建了基于自回归移动平均模型的细粒度测量模型,并提出一种启发式算法来获得精确的流量估计结果。仿真结果表明,所提的方法能够在细粒度的网络流量模型下提升流量的测量精度。

With the rapid deployment of 5G networks,the Internet of Vehicles(IoV),Internet of Things(IoT),and edge computing have made a great progress,which leads to vehicular network traffic measurements facing many challenges.To this end,the present paper studies the vehicular network traffic estimation problem for smart cities.A software-defined network(SDN)-based vehicular network traffic estimation method is proposed.A coarse-grained measurement value via an SDN architecture is designed.A fine-grained measurement model based on the autoregressive moving average(ARMA)model is constructed.Finally,a heuristic algorithm is presented to obtain accurate estimation results for vehicular network traffic.Simulation results indicate that the method proposed in this paper is feasible and effective.