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Title: LSTM-Based Proactive Congestion Management for Internet of Vehicle Networks
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.  more » « less
Award ID(s):
2120442
PAR ID:
10639043
Author(s) / Creator(s):
 ;  ;  ;  
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Vehicular Technology Conference
ISSN:
2577-2465
ISBN:
979-8-3315-1778-6
Page Range / eLocation ID:
1 to 6
Subject(s) / Keyword(s):
V2X IoV Safety Congestion Management Machine Learning NS3 SUMO LSTM
Format(s):
Medium: X
Location:
Washington, DC, USA
Sponsoring Org:
National Science Foundation
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