用LSTM提前预测车联网拥塞，提升安全数据传输效率。
LSTM-Based Proactive Congestion Management for Internet of Vehicle Networks
- 基于LSTM构建主动拥塞预测框架，利用历史流量数据预判拥堵。
- 在SUMO+NS3仿真中实现高精度拥塞预测，支持包的聚类与优先级划分。
- 适合车联网安全通信、交通系统优化等场景的开发者与研究者。
车联网（IoV）支持多种安全、娱乐及商业应用，通过车辆与路侧单元（RSU）之间的连接实现。网络拥塞管理对IoV至关重要，直接影响交通系统效率和安全关键数据的及时可靠传输。本文提出一种面向IoV网络的主动拥塞管理框架，生成拥塞场景与数据集，利用LSTM进行拥塞预测。提出框架与包拥塞数据集，并在SUMO与NS3联合仿真环境中验证其有效性，结果显示该框架能准确预测IoV网络拥塞，并通过循环神经网络实现数据包的聚类与优先级排序。
原文摘要 · Abstract (English)
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.
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