Improved PBFT Algorithm Based on Vague Sets

Abstract

The traditional PBFT consensus algorithm has several limitations in the consortium blockchain environment, such as unclear selection of primary node, excessive communication times, etc. To solve these limitations, an improved consensus algorithm VS-PBFT based on vague sets was proposed. VS-PBFT has three phases: node partition, primary node selection, and global consensus. Firstly, the nodes of the whole network are partitioned using the consistent hashing-like consensus algorithm, and then the local primary node is selected by the primary node selection algorithm in each partition. The local primary nodes run the four-phase PBFT consensus algorithm to complete the global consensus. The analysis of the VS-PBFT consistency algorithm shows that the algorithm can improve the fault-tolerant rate and reduce communication complexity, and the algorithm is dynamic; that is, node can join and quit adaptively.

An Efficient and Scalable Byzantine Fault-Tolerant Consensus Mechanism Based on Credit Scoring and Aggregated Signatures

“…In response to some of the problems arising from PBFT, such as the Byzantine general problem [18], research scholars have conducted a large number of studies and improved it. For example, G. Xu et al for the PBFT algorithm in which the master node selection is unclear and the communication complexity is too high, put forward a fuzzy-set-based improved algorithm VS-PBFT, redistribute the consensus process, use a consistent hash class consensus algorithm to partition the nodes of the whole network, and in each partition to make the master node selection to complete the global consensus, but there will be many limitations in practical applications, and the effect needs to be verified in real-world environments [19]. Y. Wang et al proposed an improved creditworthinessbased PBFT consensus algorithm (CPBFT), which introduces a credit rating and a credit coefficient so that the probability of each node being selected as a master node is affected by its past behaviors, and it is more likely that a reliable master node will be selected, but the number of nodes participating in the consensus is too many, which leads to too much complexity in communication [20].…”

“…( 6) Credit recovery mechanism: To prevent the node's credit value from always being at a high level, even if it has done evil many times in a row, it still cannot reduce the possibility of it serving as the master node, resulting in the impact of the consensus process in multiple rounds, the credit regression coefficient is set to avoid the node being punished by too high credit value and still unable to avoid it from joining the consensus after multiple rounds. The credit recovery equation is shown in Equation (18) and Equation (19).…”

“…The specific hardware and software version parameters and configuration information are shown in Table 6 and Table 7. The experiments implement PBFT, CPBFT [20], G-PBFT [22], VS-PBFT [19], RB-BFT [29] and CA-PBFT algorithms, and the experiments test the performance of the CA-PBFT algorithm through the comparative analysis of the communication complexity, the signature construction efficiency, and the signature verification efficiency, as well as the comparison of the consensus delay, node joining consensus delay, node exiting consensus delay, and the reliability. The comparison of the above algorithms is shown in Table 8.…”

A Study on Practical Byzantine Algorithms Based on Short Group Signatures

“…Guangxia Xu et al [9] implemented an improved fuzzy set-based PBFT consensus algorithm. This algorithm consist s of three phases: node partitioning, masternode selection, a nd global consensus.…”

An improved PBFT consensus algorithm based on grouping and credit grading

“…Experiments show that the CPBFT algorithm reduces the data transmission and improves the throughput compared to the PBFT algorithm. Yang Jian et al 23 and Xu Guangxia et al 24 grouped consistent nodes using a hashing algorithm, which can avoid a lot of communication between nodes, reduce the communication complexity of the network, and improve the scalability of the network, but cannot identify Byzantine nodes.…”