The rapid growth in the transportation sector has led to the emergence of smart vehicles that are equipped with ICT. These modern smart vehicles are connected to the Internet to access various services such as road condition information, infotainment, and energy management. This kind of scenario can be viewed as a vehicular cyber-physical system (VCPS) where the vehicles are at the physical layer and services are at the cyber layer. However, network traffic management is the biggest issue in the modern VCPS scenario as the mismanagement of network resources can degrade the quality of service (QoS) for end users. To deal with this issue, we propose a software defined networking (SDN)-enabled approach, named SeDaTiVe, which uses deep learning architecture to control the incoming traffic in the network in the VCPS environment. The advantage of using deep learning in network traffic control is that it learns the hidden patterns in data packets and creates an optimal route based on the learned features. Moreover, a virtual-controller-based scheme for flow management using SDN in VCPS is designed for effective resource utilization. The simulation scenario comprising 1000 vehicles seeking various services in the network is considered to generate the dataset using SUMO. The data obtained from the simulation study is evaluated using NS-2, and proves that the proposed scheme effectively handles real-time incoming requests in VCPS. The results also depict the improvement in performance on various evaluation metrics like delay, throughput, packet delivery ratio, and network load by using the proposed scheme over the traditional SDN and TCP/IP protocol suite.

中文翻译：

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SeDaTiVe：支持 SDN 的深度学习架构，用于车辆网络物理系统中的网络流量控制


交通运输行业的快速增长导致了配备信息通信技术的智能汽车的出现。这些现代智能车辆连接到互联网以访问路况信息、信息娱乐和能源管理等各种服务。这种场景可以被视为车辆信息物理系统（VCPS），其中车辆位于物理层，服务位于网络层。然而，网络流量管理是现代 VCPS 场景中最大的问题，因为网络资源管理不善可能会降低最终用户的服务质量 (QoS)。为了解决这个问题，我们提出了一种支持软件定义网络（SDN）的方法，名为 SeDaTiVe，它使用深度学习架构来控制 VCPS 环境中网络中的传入流量。在网络流量控制中使用深度学习的优势在于，它可以学习数据包中的隐藏模式，并根据学习到的特征创建最佳路由。此外，在 VCPS 中使用 SDN 设计了基于虚拟控制器的流量管理方案，以实现资源的有效利用。模拟场景包括 1000 辆在网络中寻求各种服务的车辆，被认为使用 SUMO 生成数据集。使用 NS-2 评估从仿真研究中获得的数据，并证明所提出的方案可以有效地处理 VCPS 中的实时传入请求。结果还描述了使用所提出的方案相对于传统 SDN 和 TCP/IP 协议套件在延迟、吞吐量、数据包传递率和网络负载等各种评估指标上的性能改进。