Enhancing Dynamic Behaviour in Vehicular Ad Hoc Networks through Game Theory and Machine Learning for Reliable Routing
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.
Downloads
References
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58.
Dua, D., & Graff, C. (2019). UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences.
Géron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media, Inc.
Hodge, V. J., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85-126.
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of educational psychology, 24(6), 417.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer Science & Business Media.
Kim, H., & Choi, B. (2009). A neural network approach for intrusion detection system using unsupervised feature extraction. Expert Systems with Applications, 36(5), 9197-9205.
Kriegel, H. P., Kroger, P., Schubert, E., & Zimek, A. (2009). Outlier detection in axis-parallel subspaces of high dimensional data. Proceedings of the 2009 SIAM International Conference on Data Mining, 1-12.
Langkvist, M., Karlsson, L., & Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42, 11-24.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Li, L., & Chen, J. (2018). A survey on semi-supervised learning. Data Mining and Knowledge Discovery, 32(3), 1-47.
Liao, W., Jia, K., & Zhao, G. (2019). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 184-195.
Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining, 413-422.
Pimentel, M. A., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215-249.
Vegesna, V. V. (2019). Investigations on Different Security Techniques for Data Protection in Cloud Computing using Cryptography Schemes. Indo-Iranian Journal of Scientific Research (IIJSR) Volume, 3, 69-84.
Vegesna, V. V. (2020). Secure and Privacy-Based Data Sharing Approaches in Cloud Computing for Healthcare Applications. Mediterranean Journal of Basic and Applied Sciences (MJBAS) Volume, 4, 194-209.
Vegesna, V. V. (2021). Analysis of Data Confidentiality Methods in Cloud Computing for Attaining Enhanced Security in Cloud Storage. Middle East Journal of Applied Science & Technology, 4(2), 163-178.
Vegesna, V. V. (2021). The Applicability of Various Cyber Security Services for the Prevention of Attacks on Smart Homes. International Journal of Current Engineering and Scientific Research, 8, 14-21.
Rasmussen, C. E., & Williams, C. K. (2006). Gaussian processes for machine learning. MIT press.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Fei-Fei, L. (2015). ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211-252.
Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT press.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929-1958.
Steinwart, I., & Christmann, A. (2008). Support vector machines. Springer Science & Business Media.
Tang, J., Alelyani, S., & Liu, H. (2014). Data classification: Algorithms and applications. CRC Press.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Xu, L., & Li, G. (2019). Anomaly detection in wireless sensor networks: A survey. Journal of Internet Technology, 20(2), 575-589.
Yang, Y., & Liu, Y. (1999). A re-examination of text categorization methods. Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, 42-49.
Singh, K. Artificial Intelligence & Cloud in Healthcare: Analyzing Challenges and Solutions Within Regulatory Boundaries.
Zimek, A., Schubert, E., & Kriegel, H. P. (2012). A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining, 5(5), 363-387.
