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Top1. Introduction
Vehicular Ad Hoc Networks (VANETs) represent a cutting-edge approach to wireless communication, leveraging advancements in device technology to facilitate intelligent communication between vehicles. Over recent decades, the emergence of VANETs has captured significant attention within the traffic research community. This novel communication paradigm offers promising avenues for enhancing Intelligent Transportation Systems, as evidenced by its potential applications in public transport management (Paquet 2010). Additionally, VANETs play a crucial role in bolstering transportation security, thereby mitigating the occurrence of accidents and disasters. To this end, various safety mechanisms have been devised for VANETs, encompassing functionalities such as emergency alerts, accident notifications, curve warnings, file-sharing, internet connectivity, and targeted advertisements.
Improving security in VANETs is primarily accomplished through the exchange of Basic Safety Messages (BSMs), commonly referred to as beacons, between vehicles. These beacons serve as vital communication tools, with vehicles regularly broadcasting them to relay essential information such as their position, speed, and direction within the network. In critical situations like collisions, accidents, or road surface collapses, vehicles also transmit emergency beacons or safety messages to alert nearby vehicles. However, in densely populated vehicular environments, the sheer volume of beacons can lead to congestion in the communication channel, resulting in an increased likelihood of message loss and delays. This congestion not only hampers vehicles' awareness but also diminishes the accuracy of safety-related information. The growing rate of beacon transmissions exacerbates this issue, raising concerns about the channel's capacity to handle the escalating data load effectively. Given these challenges, the development of robust congestion control strategies for VANETs has garnered significant attention in recent years. Effectively managing channel congestion is crucial for ensuring timely and reliable message delivery, particularly as vehicular density continues to rise.
The endeavor to model vehicle behavior in VANETs analytically has become a focal point of research interest, with increasing attention from scholars. Numerous analytical models have been proposed to scrutinize VANET performance and offer viable solutions tailored to the unique challenges encountered in these networks. Among these challenges, congestion control stands out as a significant concern in computer networks. Metrics commonly employed to assess congestion control include fairness among vehicles, convergence time, and oscillation size (Chiu and Jain 1989). In the context of VANETs, congestion control must operate in a decentralized manner, without relying on any centralized infrastructure. This decentralized approach is essential to accommodate the dynamic nature of VANETs. Additionally, the convergence time of the control mechanism must be minimized to swiftly adapt to changing network conditions and ensure efficient traffic management.