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Efficacy Artificial Bee Colony Optimization-Based Gaussian AOMDV (EABCO-GAOMDV) Routing Protocol for Seamless Traffic Rerouting in Stochastic Vehicular Ad Hoc Network


Affiliations
1 Department of Computer Science, Sri Krishna Adithya College of Arts and Science, Coimbatore, Tamil Nadu, India
2 Department of Computer Science, Government Arts and Science College for Women, Coimbatore, Tamil Nadu, India
 

Vehicular Ad Hoc Networks (VANETs) have emerged as a dynamic communication paradigm enabling vehicles to form temporary Ad Hoc networks for seamless information exchange. Stochastic VANETs (SVANETs) introduce complexities due to their stochastic nature, necessitating innovative strategies to handle dynamic traffic conditions and intermittent connectivity. Routing within SVANETs presents unique challenges arising from uncertainties inherent in real-world scenarios. The stochastic environment gives rise to intermittent connectivity, dynamic traffic conditions, and varying network topologies. Traditional routing protocols struggle to provide efficient and reliable solutions under these challenging circumstances. This paper introduces the Efficacy Artificial Bee Colony Optimization-Based Gaussian AOMDV (EABCO-GAOMDV) routing protocol as a promising solution for the routing challenges in SVANETs. The protocol integrates the intelligence of Artificial Bee Colony Optimization (EABCO) with the adaptive characteristics of Gaussian AOMDV, aiming to enhance the efficiency of route discovery and rerouting. Through extensive simulations encompassing diverse SVANET scenarios, EABCO-GAOMDV is rigorously evaluated for performance and effectiveness. The protocol substantially improves route stability, packet delivery ratio, and end-to-end delay. The simulation results unequivocally validate the protocol’s ability to adapt to stochastic conditions, ensuring effective traffic rerouting and heightened network resilience. EABCO-GAOMDV showcases its potential as a robust routing solution for SVANETs, effectively addressing the challenges of stochastic conditions.

Keywords

Ad Hoc Network, Bio-inspired Optimization, Routing, Stochastic, VANET, AOMDV, SVANET, EABCO-GAOMDV, Vehicle.
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  • Efficacy Artificial Bee Colony Optimization-Based Gaussian AOMDV (EABCO-GAOMDV) Routing Protocol for Seamless Traffic Rerouting in Stochastic Vehicular Ad Hoc Network

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Authors

M. Kayalvizhi
Department of Computer Science, Sri Krishna Adithya College of Arts and Science, Coimbatore, Tamil Nadu, India
S. Geetha
Department of Computer Science, Government Arts and Science College for Women, Coimbatore, Tamil Nadu, India

Abstract


Vehicular Ad Hoc Networks (VANETs) have emerged as a dynamic communication paradigm enabling vehicles to form temporary Ad Hoc networks for seamless information exchange. Stochastic VANETs (SVANETs) introduce complexities due to their stochastic nature, necessitating innovative strategies to handle dynamic traffic conditions and intermittent connectivity. Routing within SVANETs presents unique challenges arising from uncertainties inherent in real-world scenarios. The stochastic environment gives rise to intermittent connectivity, dynamic traffic conditions, and varying network topologies. Traditional routing protocols struggle to provide efficient and reliable solutions under these challenging circumstances. This paper introduces the Efficacy Artificial Bee Colony Optimization-Based Gaussian AOMDV (EABCO-GAOMDV) routing protocol as a promising solution for the routing challenges in SVANETs. The protocol integrates the intelligence of Artificial Bee Colony Optimization (EABCO) with the adaptive characteristics of Gaussian AOMDV, aiming to enhance the efficiency of route discovery and rerouting. Through extensive simulations encompassing diverse SVANET scenarios, EABCO-GAOMDV is rigorously evaluated for performance and effectiveness. The protocol substantially improves route stability, packet delivery ratio, and end-to-end delay. The simulation results unequivocally validate the protocol’s ability to adapt to stochastic conditions, ensuring effective traffic rerouting and heightened network resilience. EABCO-GAOMDV showcases its potential as a robust routing solution for SVANETs, effectively addressing the challenges of stochastic conditions.

Keywords


Ad Hoc Network, Bio-inspired Optimization, Routing, Stochastic, VANET, AOMDV, SVANET, EABCO-GAOMDV, Vehicle.

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DOI: https://doi.org/10.22247/ijcna%2F2023%2F223694