Enhancing Dynamic Behaviour in Vehicular Ad Hoc Networks through Game Theory and Machine Learning for Reliable Routing

Authors

  • Padmaja Pulivarthy Author

Abstract

Vehicular Ad Hoc Networks (VANETs) represent a pivotal element in modern intelligent transportation systems, providing the foundation for vehicle-to-vehicle and vehicle-to-infrastructure communication. Ensuring reliable and stable routing within these networks is paramount for enhancing road safety, traffic management, and the overall efficiency of transportation systems. This paper explores an innovative approach to improving the dynamic behavior of VANETs by integrating game theory and machine learning techniques. In this research, game theory is utilized to model the interactions between vehicles as a strategic game, where each vehicle aims to optimize its routing decisions based on the behavior of other network participants. By applying concepts such as Nash equilibrium, we analyze and predict the optimal strategies for vehicles under various traffic conditions. Concurrently, machine learning algorithms are employed to adaptively learn from the network environment, allowing for real-time adjustments to routing strategies based on historical data and current network states. The proposed methodology involves the development of a hybrid framework that leverages game-theoretic models to determine optimal routing strategies and machine learning techniques to enhance these strategies through continuous learning and adaptation. Specifically, reinforcement learning algorithms are integrated to dynamically adjust routing decisions, providing a robust mechanism to handle the inherent variability and unpredictability of VANETs. Simulation results demonstrate that the integration of game theory and machine learning significantly improves the reliability and stability of routing in VANETs. The hybrid approach not only reduces packet loss and end-to-end delay but also enhances overall network throughput. Additionally, the adaptability of the proposed system ensures its effectiveness in diverse and rapidly changing traffic scenarios. This study contributes to the field by presenting a comprehensive solution that addresses the challenges of dynamic behavior in VANETs through a synergistic application of game theory and machine learning. The findings have the potential to significantly advance the development of intelligent transportation systems, providing a foundation for future research and practical implementations aimed at achieving safer and more efficient vehicular communication networks.

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Published

2023-12-14

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Articles

How to Cite

Enhancing Dynamic Behaviour in Vehicular Ad Hoc Networks through Game Theory and Machine Learning for Reliable Routing. (2023). International Journal of Machine Learning and Artificial Intelligence, 4(4), 1-13. https://jmlai.in/index.php/ijmlai/article/view/40

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