PROJECT TITLE :

Hybrid Genetic Firefly Algorithm-Based Routing Protocol for VANETs

ABSTRACT:

In order to facilitate effective communication between the vehicles to vehicle (V2V) infrastructure, vehicular ad hoc networks, also known as VANETs, are utilized. At the moment, VANETs are struggling with issues pertaining to node management, security, and routing in V2V communication. Research opportunities in routing, security, and mobility management in VANETs have increased as a result of the implementation of intelligent transportation systems. The optimization of routing in VANETs for desired traffic scenarios is one of the most significant challenges that they face. Traditional protocols, such as Adhoc On-demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination Sequence Distance Vector (DSDV), are ideal for use with generic mobile nodes; however, they are not suitable for use with VANET due to the high degree of vehicle mobility and the dynamic nature of its movement. In a similar vein, swarm intelligence routing algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) routing techniques are partially successful when it comes to addressing optimized routing for sparse, dense, and realistic traffic network scenarios in VANET. In addition, the vast majority of metaheuristics approaches have issues with premature convergence, getting stuck in local optima, and slow convergence speed. In order to facilitate quicker communication within VANET, a Hybrid Genetic Firefly Algorithm-based Routing Protocol (HGFA) has been suggested as a solution. Combining aspects of the Genetic Algorithm (GA) with those of the Firefly algorithm allows for the implementation of VANET routing that is applicable to both sparse and dense network configurations. An exhaustive analysis of comparative performance reveals that the proposed HGFA algorithm outperforms the Firefly and PSO techniques by 0.77% and 0.55% of significance in dense network scenarios, and by 0.74% and 0.42% in sparse network scenarios, respectively. These results are based on comparisons of the performance of each method in a variety of network configurations.