Abstract
Vehicular Adhoc Networks (VANETs) are used for efficient communication among the vehicles to vehicle (V2V) infrastructure. Currently, VANETs are facing node management, security, and routing problems in V2V communication. Intelligent transportation systems have raised the research opportunity in routing, security, and mobility management in VANETs. One of the major challenges in VANETs is the optimization of routing for desired traffic scenarios. Traditional protocols such as Adhoc On-demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination Sequence Distance Vector (DSDV) are perfect for generic mobile nodes but do not fit for VANET due to the high and dynamic nature of vehicle movement. Similarly, swarm intelligence routing algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) routing techniques are partially successful for addressing optimized routing for sparse, dense, and realistic traffic network scenarios in VANET. Also, the majority of metaheuristics techniques suffer from premature convergence, being stuck in local optima, and poor convergence speed problems. Therefore, a Hybrid Genetic Firefly Algorithm-based Routing Protocol (HGFA) is proposed for faster communication in VANET. Features of the Genetic Algorithm (GA) are integrated with the Firefly algorithm and applied in VANET routing for both sparse and dense network scenarios. Extensive comparative analysis reveals that the proposed HGFA algorithm outperforms Firefly and PSO techniques with 0.77% and 0.55% of significance in dense network scenarios and 0.74% and 0.42% in sparse network scenarios, respectively.
Highlights
Vehicular Adhoc Networks (VANETs) can be used as a driver’s assistance for communication and coordination among each other that will minimize the critical situation in V2V communication, e.g., random braking, obstacles, accidents on the road, bumper to bumper jams, random increase in speed, pathways for emergency vehicles like fire, police, and ambulance
Crash Avoidance Matrices Partnership (CAMP), Advance Driver Assistance System (ADASE), FLEETNET, and CARTALK are some of the famous applications that are developed by various automobile manufacturers and governments through public-private partnerships [1]
Comparative analysis have revealed that the developed approach has shown better performance in transmission time with 0.77% and 0.55% of significance in dense network scenarios and 0.74% and 0.42 % in a sparse network scenario in comparison with the existing VANET routing algorithms such as standard Firefly and Particle Swarm Optimization (PSO)
Summary
Vehicular Adhoc Networks (VANETs) can be used as a driver’s assistance for communication and coordination among each other that will minimize the critical situation in V2V communication, e.g., random braking, obstacles, accidents on the road, bumper to bumper jams, random increase in speed, pathways for emergency vehicles like fire, police, and ambulance. 1. Hybrid Genetic Firefly Algorithm-based Routing Protocol (HGFA) is proposed for faster communication in VANET. The majority of metaheuristics techniques suffer from premature convergence [2], [11], [21], being stuck in local optima [7], [12], [13], and poor convergence speed [2], [7], [14], [15] problems To overcome these issues, a Hybrid Genetic Firefly Algorithm-based Routing Protocol (HGFA) is proposed for VANETs. III. This research work presents the proposed communication model that fits for sparse as well as dense network traffic scenarios in VANETs. C. PROPOSED APPROACH TO OPTIMIZE THE VANET ROUTING IN DISTINCTIVE NETWORK SCENARIOS In this approach, each node (vehicle) is considered a firefly. The steps of the proposed algorithm are represented in Algorithm 2
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