An Optimal Navigation Model for Realistic Traffic Network Scenarios in VANET

Abstract

Vehicle Ad-hoc Networks (VANETs) are a kind of Internet of Things system where groups of vehicles connect and vehicle tracking infrastructure to enhance driver and public security as well as lifestyles. In a VANET, vehicles oversee the communicating status to the controller and other drivers for processing, as well as the statuses of the road, traffic, and atmosphere around them. In these actual VANET network traffic scenarios, this research presents the mixing of enhanced genetic algorithms (EGA) based on ant colony optimization (ACO) techniques (EGAACO) to create an optimized routing algorithm. This research evaluates the conventional algorithm with metaheuristic possibilities and includes the experimental VANET simulation scenario. To verify the outcomes, the proposed approach is tested using open-source network and traffic simulation tools. On Simulation of Urban Mobility (SUMO), all three different traffic scenarios were simulated and examined using NS3.2. The results of these analyses were satisfactory, and it emerged EGAACO algorithm, performed better than the others in each of the three traffic scenarios. The four real-time traffic network scenarios include average throughput, packet delivery ratio, end-to-end delay, and packet loss in a network, which is collected from the city of Bangalore, and the performance metric measures utilized in traffic network conditions. The experimental result proved that the proposed EGAACO algorithm outperforms the Ad-hoc on- Demand Distance Vector Routing (AODV), Particle Swarm Intelligence (PSO), and ACO routing protocols in all settings.