Vehicle-to-everything (V2X) networks support a variety of safety, entertainment, and commercial applications. This is realized by applying the principles of the Internet of Vehicles (IoV) to facilitate connectivity among vehicles and between vehicles and roadside units (RSUs). Network congestion management is essential for IoVs and it represents a significant concern due to its impact on improving the efficiency of transportation systems and providing reliable communication among vehicles for the timely delivery of safety-critical packets. This paper introduces a framework for proactive congestion management for IoV networks. We generate congestion scenarios and a data set to predict the congestion using LSTM. We present the framework and the packet congestion dataset. Simulation results using SUMO with NS3 demonstrate the effectiveness of the framework for forecasting IoV network congestion and clustering/prioritizing packets employing recurrent neural networks.

翻译：车联网（V2X）网络支持各类安全、娱乐及商业应用，其实现依托车联网（IoV）技术原理，促进车辆之间以及车辆与路边单元（RSU）之间的互联。网络拥塞管理对车联网至关重要，因其直接影响交通系统效率的提升以及车辆间安全关键数据包的可靠及时传输，已成为该领域的核心问题。本文提出一种面向车联网的主动拥塞管理框架。我们通过生成拥塞场景及数据集，利用长短期记忆网络（LSTM）实现拥塞预测。文中详细阐述了该框架及数据包拥塞数据集。基于SUMO与NS3的仿真结果表明，该框架能有效预测车联网网络拥塞，并借助循环神经网络实现数据包的聚类与优先级划分。