R-PBFT: Efficient DAG-based consensus Algorithm for Internet of Vehicles

Kedong Niu, Yangjie Cao, Jie Li
Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, PMLR 278:587-594, 2025.

Abstract

With the increase in the number of vehicles in residential households, the traffic flow on roads has shown a significant growth trend, which places higher demands on the capacity of vehicular networks. It is worth noting that the traditional PBFT algorithm experiences a significant decline in consensus efficiency as the number of nodes increases, which may pose a key constraint on the information exchange efficiency in vehicular networks. Based on this, this study proposes an R-PBFT consensus algorithm based on DAG blockchain. This algorithm optimizes the consensus mechanism and significantly improves the information transmission rate between roadside units and vehicles. Experimental results show that, compared to traditional consensus algorithms, the proposed solution effectively improves consensus efficiency while reducing communication costs.

Cite this Paper

BibTeX
@InProceedings{pmlr-v278-niu25a, title = {R-PBFT: Efficient DAG-based consensus Algorithm for Internet of Vehicles}, author = {Niu, Kedong and Cao, Yangjie and Li, Jie}, booktitle = {Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing}, pages = {587--594}, year = {2025}, editor = {Zeng, Nianyin and Pachori, Ram Bilas and Wang, Dongshu}, volume = {278}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v278/main/assets/niu25a/niu25a.pdf}, url = {https://proceedings.mlr.press/v278/niu25a.html}, abstract = {With the increase in the number of vehicles in residential households, the traffic flow on roads has shown a significant growth trend, which places higher demands on the capacity of vehicular networks. It is worth noting that the traditional PBFT algorithm experiences a significant decline in consensus efficiency as the number of nodes increases, which may pose a key constraint on the information exchange efficiency in vehicular networks. Based on this, this study proposes an R-PBFT consensus algorithm based on DAG blockchain. This algorithm optimizes the consensus mechanism and significantly improves the information transmission rate between roadside units and vehicles. Experimental results show that, compared to traditional consensus algorithms, the proposed solution effectively improves consensus efficiency while reducing communication costs.} }
Endnote
%0 Conference Paper %T R-PBFT: Efficient DAG-based consensus Algorithm for Internet of Vehicles %A Kedong Niu %A Yangjie Cao %A Jie Li %B Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2025 %E Nianyin Zeng %E Ram Bilas Pachori %E Dongshu Wang %F pmlr-v278-niu25a %I PMLR %P 587--594 %U https://proceedings.mlr.press/v278/niu25a.html %V 278 %X With the increase in the number of vehicles in residential households, the traffic flow on roads has shown a significant growth trend, which places higher demands on the capacity of vehicular networks. It is worth noting that the traditional PBFT algorithm experiences a significant decline in consensus efficiency as the number of nodes increases, which may pose a key constraint on the information exchange efficiency in vehicular networks. Based on this, this study proposes an R-PBFT consensus algorithm based on DAG blockchain. This algorithm optimizes the consensus mechanism and significantly improves the information transmission rate between roadside units and vehicles. Experimental results show that, compared to traditional consensus algorithms, the proposed solution effectively improves consensus efficiency while reducing communication costs.
APA
Niu, K., Cao, Y. & Li, J.. (2025). R-PBFT: Efficient DAG-based consensus Algorithm for Internet of Vehicles. Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 278:587-594 Available from https://proceedings.mlr.press/v278/niu25a.html.

Related Material