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Improving the Quality of Vanet Communication Using Federated Peer-to-Peer Learning
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Vehicular Ad hoc Networks (VANETs) are one of the most advanced transportation networks that have attracted much attention in recent years. The VANETs are characterized by a large number of traffic flows, which make them a good choice for a wide range of applications. However, due to the unique characteristics of the VANET, routing algorithms present a significant obstacle that must be surmounted. In order to improve the communication quality, the research uses federated learning. The research demonstrates the capacity of the model to learn from its previous errors while also delivering more accurate projections using the federated learning. The findings of the simulation demonstrate that the model with a prediction accuracy of 4 packets/s has the highest accuracy when compared to its contemporaries as well as other predicted models. The results show that the proposed method achieves higher rate of accuracy in transmitting the packets with reduced overhead than the other existing methods.
Keywords
Communication Quality, VANET, Federated Learning, Overhead.
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- J. Shu and M. Guizani, “Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach”, IEEE Transactions on Intelligent Transportation Systems, Vol. 22, No. 7, pp. 4519-4530, 2020.
- A. Mchergui, “Relay Selection based on Deep Learning for Broadcasting in VANET”, Proceedings of International Conference on Wireless Communications and Mobile Computing, pp. 865-870, 2019.
- B. Karthiga and A. Hariharasudan, “Intelligent Intrusion Detection System for VANET using Machine Learning and Deep Learning Approaches”, Wireless Communications and Mobile Computing, Vol. 2022, pp. 1-13, 2022.
- G. Kaur and D. Kakkar, “Hybrid Optimization Enabled Trust-based Secure Routing with Deep Learning-based Attack Detection in VANET”, Ad Hoc Networks, Vol. 136, pp. 102961-102976, 2022.
- A. Mchergui and S. Zeadally, “Survey on Artificial Intelligence (AI) Techniques for Vehicular Ad-Hoc Networks (VANETs)”, Vehicular Communications, Vol. 34, pp. 100403-100415, 2022.
- Md Habibur Rahman and Mohammad Nasiruddin, “Impact of Two Realistic Mobility Models for Vehicular Safety Applications”, Proceedings of International Conference on Informatics, Electronics and Vision, pp. 1-6, 2014.
- N. Bouchema, R. Naja and A. Tohme, “Traffic Modeling and Performance Evaluation in Vehicle to Infrastructure 802.11p Network”, Proceedings of International Conference on Ad Hoc Networks, pp. 82-99, 2014.
- S.M. Tornell, C.T. Calafate, J.C. Cano and P. Manzoni, “DTN Protocol for Vehicular Networks: An Application Oriented Overview”, IEEE Communications Surveys and Tutorials, Vol. 17, No. 2, pp. 868-887, 2014.
- A. Nahar and D. Das, “SeScR: SDN-Enabled Spectral Clustering-Based Optimized Routing using Deep Learning in VANET Environment”, Proceedings of IEEE International Symposium on Network Computing and Applications, pp. 1-9, 2020.
- P. Rani, N. Sharma and P.K. Singh, “Performance Comparisons of VANET Routing Protocols”, Proceedings of IEEE International Conference on Wireless Communications, Networking and Mobile Computing, pp. 23-28, 2011.
- S.S. Sepasgozar and S. Pierre, “Network Traffic Prediction Model Considering Road Traffic Parameters using Artificial Intelligence Methods in VANET”, IEEE Access, Vol. 10, pp. 8227-8242, 2022.
- W. Viriyasitavat, M. Boban, H.M. Tsai and A. Vasilakos, “Vehicular Communications: Survey and Challenges of Channel and Propagation Models”, IEEE Vehicular Technology Magazine, Vol. 10, No. 2, pp. 55-66, 2015.
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