An improved PBFT consensus algorithm based on grouping and credit grading

Author:

Liu Shannan,Zhang Ronghua,Liu Changzheng,Xu Chenxi,Wang Jiaojiao

Abstract

AbstractTo improve the blockchain consensus algorithm practical Byzantine fault tolerance (PBFT) with random master node selection, which has high communication overhead and a small supported network size, this paper proposes a Byzantine fault tolerant consensus algorithm based on credit (CBFT) enhanced with a grouping and credit model. The CBFT algorithm divides the network nodes according to the speed of their response to the management nodes, resulting in different consensus sets, and achieves consensus within and outside the group separately to reduce communication overhead and increase system security. Second, the nodes are divided into different types according to the credit model, each with different responsibilities to reduce the probability that the master node is a malicious node. Experimental results show that the throughput of the CBFT algorithm is 3.1 times that of PBFT and 1.5 times that of GPBFT when the number of nodes is 52. Our scheme has latency that is 7.4% that of PBFT and 38.8% that of GPBFT; CBFT has communication overhead that is 6.4% that of PBFT and 87.3% that of GPBFT. The number of nodes is 300, and the Byzantine fault tolerance is improved by 59.3%. These improvements are clearer with the increase in the number of nodes.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference45 articles.

1. Essaid, M. et al. Network Usage of Bitcoin Full Node. 2018 International Conference on Information and Communication Technology Convergence (ICTC). 1286–1291 (2018).

2. Liu, S. et al. Improvement of the Pbft Algorithm Based On Grouping and Reputation Value Voting. Int. J. Digital Crime Forensics (IJDCF). 14, 1–15 (2022).

3. Yang, L. The blockchain: State-Of-the-art and research challenges. J. Ind. Inf. Integr. 15, 80–90 (2019).

4. Zhang, J., Zhong, S., Wang, T., Chao, H. & Wang, J. Blockchain-based systems and applications: A survey. J. Internet Technol. 21, 1–14 (2020).

5. Banafa, A. Blockchain Technology and Applications, River Publishers.

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