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Decisiveness PSO-Based Gaussian AOMDV (DPSO-GAOMDV) Routing Protocol: Smart Routing for Dynamic Traffic Conditions 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 gained prominence in vehicular communication due to their potential to enhance road safety, traffic efficiency, and infotainment services. However, the evolution of Stochastic VANETs (SVANETs) has introduced a layer of uncertainty, where vehicular interactions are influenced by dynamic factors such as varying traffic conditions, changing communication environments, and unpredictable link qualities. Routing within SVANETs presents distinct challenges stemming from the stochastic nature of the environment. Traditional routing protocols struggle to maintain reliable connections amidst fluctuating link conditions, leading to increased latency, dropped packets, and inefficient route utilization. The novel “Decisiveness PSO-Based Gaussian AOMDV (DPSO-GAOMDV) Routing Protocol” is introduced to address these challenges. This innovative protocol combines the predictive power of Gaussian-Anticipatory On-Demand Distance Vector (GAOMDV) routing with the dynamic adaptability of Particle Swarm Optimization (PSO). GAOMDV’s ability to anticipate link stability using Gaussian distribution is integrated with DPSO’s agility in optimizing routing decisions. The simulation phase of the study evaluates the DPSO-GAOMDV protocol under various stochastic vehicular scenarios. The protocol’s performance is thoroughly analyzed by emulating real-world traffic conditions and communication dynamics. The simulation results underscore the protocol’s efficacy in reducing route maintenance overhead, improved packet delivery ratios, and enhanced network stability. The predictive insights and dynamic optimization mechanisms showcase its potential to drive innovative, resilient and efficient routing strategies in the face of stochastic vehicular conditions.

Keywords

Ad Hoc Network, Bio-Inspired Optimization, Routing, Stochastic, VANET, Vehicle.
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  • Decisiveness PSO-Based Gaussian AOMDV (DPSO-GAOMDV) Routing Protocol: Smart Routing for Dynamic Traffic Conditions 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 gained prominence in vehicular communication due to their potential to enhance road safety, traffic efficiency, and infotainment services. However, the evolution of Stochastic VANETs (SVANETs) has introduced a layer of uncertainty, where vehicular interactions are influenced by dynamic factors such as varying traffic conditions, changing communication environments, and unpredictable link qualities. Routing within SVANETs presents distinct challenges stemming from the stochastic nature of the environment. Traditional routing protocols struggle to maintain reliable connections amidst fluctuating link conditions, leading to increased latency, dropped packets, and inefficient route utilization. The novel “Decisiveness PSO-Based Gaussian AOMDV (DPSO-GAOMDV) Routing Protocol” is introduced to address these challenges. This innovative protocol combines the predictive power of Gaussian-Anticipatory On-Demand Distance Vector (GAOMDV) routing with the dynamic adaptability of Particle Swarm Optimization (PSO). GAOMDV’s ability to anticipate link stability using Gaussian distribution is integrated with DPSO’s agility in optimizing routing decisions. The simulation phase of the study evaluates the DPSO-GAOMDV protocol under various stochastic vehicular scenarios. The protocol’s performance is thoroughly analyzed by emulating real-world traffic conditions and communication dynamics. The simulation results underscore the protocol’s efficacy in reducing route maintenance overhead, improved packet delivery ratios, and enhanced network stability. The predictive insights and dynamic optimization mechanisms showcase its potential to drive innovative, resilient and efficient routing strategies in the face of stochastic vehicular conditions.

Keywords


Ad Hoc Network, Bio-Inspired Optimization, Routing, Stochastic, VANET, Vehicle.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F223430