An improved algorithm for practical byzantine fault tolerance to large-scale consortium chain

•A node feature grouping model is proposed. Specifically, according to the node characteristics of the consortium chain, nodes with the same characteristics are grouped into an organization, and a feature organization and multi-feature organizations are formed into a group. There is independent consensus within the group, and consensus among groups can be reached at the same time without affecting each other. This model optimizes the node structure of large-scale consortium chain and simplifies the complexity of consensus.•A credit reward mechanism is proposed. This method divides the nodes in the group into committees and alternative committees. By introducing the credit calculation formula, the high credit master node is selected from the committee. It improves the efficiency of consensus in large consortium chains.•A consensus node selection optimization scheme is proposed. The scheme introduces node index to number and sort nodes. Establish a replacement cycle to replace the high-credit nodes in the alternative committee with the low-credit nodes in the committee. It optimizes the consensus efficiency of the consortium chain. Consortium chain can better combine blockchain technology with market business, so it is adopted by all walks of life and develops at a large scale. Practical Byzantine Fault Tolerance (PBFT) is more suitable for consortium chain, which are partially decentralized, resistant to Byzantine nodes and strong consistency. However, the limited network scale that PBFT can support is not conducive to the large-scale development of consortium chain. Based on the analysis of the working principle and consensus mechanism of PBFT, this study proposed an algorithm to improve PBFT: feature grouping and credit optimization Byzantine Fault Tolerance (FCBFT). In this algorithm, a feature grouping model is proposed to optimize the node structure of large-scale consortium chain, which divides large-scale network nodes into different institutions to form independent consensus groups by feature grouping. On this basis, a reputation score reward mechanism is proposed to improve the consensus efficiency of large-scale consortium chains. It introduces a reputation score calculation formula to select high-reputation primary nodes. At the same time, a replacement cycle is established to replace high-reputation nodes with low-reputation nodes, so as to optimize the consensus efficiency of the consortium chain. The experimental results show that FCBFT has sh