Dynamic Congestion Control Mechanisms for Enhanced Efficiency in Vehicular Ad-Hoc Networks

Abstract

The rapid advancement of Vehicular Ad-Hoc Networks (VANETs) has paved the way for enhanced vehicular communication, promoting road safety and traffic efficiency. However, the dynamic nature of vehicular environments often leads to congestion, adversely affecting network performance and driving conditions. This research paper investigates dynamic congestion control mechanisms to enhance efficiency in VANETs. The primary objective is to develop adaptive algorithms that can effectively manage data traffic, reduce latency, and improve overall network throughput in varying traffic scenarios. The proposed mechanisms leverage real-time data and machine learning techniques to predict and mitigate congestion, ensuring seamless communication between vehicles. The methodology includes extensive simulations and field tests to evaluate the performance of these mechanisms under diverse traffic conditions. Key findings indicate that the dynamic congestion control mechanisms significantly outperform traditional methods, resulting in reduced packet loss, lower communication delays, and enhanced vehicular network efficiency. The study concludes by highlighting the potential applications of these mechanisms in real-world VANET deployments and suggesting directions for future research to further optimize vehicular communication systems.