ICTACT Journal on Communication Technology

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Improving the Quality of Vanet Communication Using Federated Peer-to-Peer Learning


Affiliations
1 Department of Computer Applications, SRM Institute of Science and Technology, Tiruchirappalli, India., India
2 School of Computer Science and Information Technology, Jain (Deemed-To-Be University), India., India
3 Department of Computer Science and Engineering, S.G. Balekundri Institute of Technology, India., India
4 Department of Computer Science and Engineering, Presidency University, India., India
5 School of Information Technology, Texila American University, Zambia., Zambia
     

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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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  • Improving the Quality of Vanet Communication Using Federated Peer-to-Peer Learning

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Authors

T.R. Ramesh
Department of Computer Applications, SRM Institute of Science and Technology, Tiruchirappalli, India., India
R. Raghavendra
School of Computer Science and Information Technology, Jain (Deemed-To-Be University), India., India
Sushiladevi B. Vantamuri
Department of Computer Science and Engineering, S.G. Balekundri Institute of Technology, India., India
R. Pallavi
Department of Computer Science and Engineering, Presidency University, India., India
Balamurugan Easwaran
School of Information Technology, Texila American University, Zambia., Zambia

Abstract


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.

References