LSTM-Based Proactive Congestion Management for Internet of Vehicle Networks

TL;DR

This paper proposes an LSTM-based framework for proactive congestion management in Internet of Vehicle networks, aiming to improve communication reliability and safety by predicting network congestion before it occurs.

Contribution

It introduces a novel proactive congestion management framework using LSTM to predict congestion in IoV networks and provides a new dataset for this purpose.

Findings

LSTM effectively predicts network congestion in IoV scenarios.

The framework improves packet prioritization and clustering.

Simulation results validate the approach's effectiveness.

Abstract

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.