Enhancing Vehicular Ad Hoc Networks’ Dynamic Behavior by Integrating Game Theory and Machine Learning Techniques for Reliable and Stable Routing

Abstract

VANETs (vehicular ad hoc networks) have evolved as a platform for enabling intelligent inter-vehicle communication while also improving traffic safety and performance. VANETs are a difficult research topic because of the road dynamics, high mobility of cars, their unlimited power supply, and the growth of roadside wireless infrastructures. In wireless networks, game theory approaches have been widely used to investigate the interactions between competitive and cooperative behavior. In this research, we propose a technique for vehicular ad hoc networks that uses a game theory approach to automate vehicle grouping and cluster head nomination. This will eliminate the need for cluster reformation on a regular basis. Furthermore, each vehicle’s social behavior will be exploited to establish clusters in the vehicular environment. For the development of clusters on the social behavior of the cars, a machine learning approach (K-means algorithm) is applied. The proposed system is tested against a variety of characteristics, including CH life time, average cluster member life time, average number of reaffiliation times, throughput, and packet loss rate, and the results indicate that the VANET performed very well with high accuracy in validation and testing, and overall in the range of 0.97 to 0.99.

A QoS-Aware IoT Edge Network for Mobile Telemedicine Enabling In-Transit Monitoring of Emergency Patients

“…The simulation gives the provision to choose sender, receiver, intermediate nodes and their communication technology. Ambulances are chosen as the sender for this simulation study along with a VANET-based telemedicine architecture, with IEEE 802.11 as the radio access technology (RAT) [54][55][56]. The mobile ambulances are simulated to traverse through different types of traffic scenarios experienced in rural, suburban, and urban areas, totalling a 6km road stretch.…”

The Evolution of VANET Networks: A Review of Emerging Trends in Artificial Intelligence and Software-Defined Networks

“…In [23], they focus on the use of machine learning in Vanet, particularly in its applicability to security and communication networks. A methodology for vehicular machine learning is presented, and various models and systematic techniques are reviewed.…”

Blockchain-Assisted Privacy-Preserving and Context-Aware Trust Management Framework for Secure Communications in VANETs

“…It has been shown that most accidents can be avoided by warning drivers one half second in advance [ 4 ]. Vehicular ad hoc networks (VANETs) have been developed recently to reduce accidents, improve traffic efficiency and road safety, and enhance user comfort [ 5 ]. VANETs enable vehicles to communicate with other vehicles via Vehicle-to-Vehicle (V2V) communication, and to communicate with roadside units (RSUs) via Vehicle-to-Infrastructure (V2I) communication, to exchange information and inform drivers about road hazards.…”