We are introducing an innovative networking approach to the Internet of Vehicles (IoV) for their automation and orchestration by redesigning the wireless edge framework. To this end, we propose to expand the Software Defined Networking (SDN) functionality from the edge network to the cars, i.e. equipping our vehicles with mobile base stations having SDN capabilities. By adopting such a novel approach, most of the impending IoV automation and orchestration challenges are ameliorated, which has been extremely difficult and non-trivial to tackle by using standard existing approaches. The three primary challenges identified in this context are a) the scalability of the network of connected autonomous vehicles, b) the desirable security of the IoV data networking, and c) the flexibility of quality of experience (QoE) perceived by the vehicles. To tackle the aforementioned three challenges, we design a policy-driven framework for a secure and efficient IoV networking paradigm and then investigate its performance via analytic modeling. More importantly, from the IoV data networking perspective, we design an intent-based flow offloading scheme to facilitate enhanced and adjustable QoE. Furthermore, we develop a rigorous analysis to quantify the efficiency of the proposed SDN-capable IoV data networking by modeling TCP connections over WiFi. Using mathematics as a tool for reasoning the three key challenges, our feasibility analysis is validated with extensive simulations. Finally, we provide new insights by deriving the stability conditions for the data flow dynamics of the proposed approach.

中文翻译：

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用于自动化和编排的软件定义车联网


我们正在通过重新设计无线边缘框架，向车联网 (IoV) 引入创新的网络方法，以实现其自动化和编排。为此，我们建议将软件定义网络（SDN）功能从边缘网络扩展到汽车，即为我们的车辆配备具有SDN功能的移动基站。通过采用这种新颖的方法，大多数迫在眉睫的车联网自动化和编排挑战都得到了缓解，而使用标准的现有方法来解决这些挑战非常困难且不简单。在此背景下确定的三个主要挑战是：a）联网自动驾驶车辆网络的可扩展性，b）车联网数据网络的理想安全性，以及c）车辆感知的体验质量（QoE）的灵活性。为了解决上述三个挑战，我们为安全高效的车联网网络范式设计了一个策略驱动的框架，然后通过分析建模研究其性能。更重要的是，从车联网数据网络的角度来看，我们设计了一种基于意图的流量分流方案，以促进增强和可调整的QoE。此外，我们还进行了严格的分析，通过对 WiFi 上的 TCP 连接进行建模来量化所提出的支持 SDN 的车联网数据网络的效率。使用数学作为推理三个关键挑战的工具，我们的可行性分析通过广泛的模拟得到验证。最后，我们通过推导所提出方法的数据流动态的稳定性条件来提供新的见解。