## 3 Improved RE-BPFT Blockchain Consensus Algorithm
### View Change Protocol
### Communication Overhead Testing

In terms of communication load, this section presents comparative experimental results for PBFT, RBFT, PPoR, and the proposed RE-PBFT algorithm. Through multiple experiments under varying operational scenarios, communication overhead was evaluated. By adjusting node numbers within the consortium blockchain, network traffic generated during the consensus process was measured. Communication overhead--the resource and cost expenditure associated with message transmissions and inter-node communications--was employed as a crucial indicator for consensus algorithm efficiency. A lower communication overhead indicates a more efficient consensus protocol, reducing network load and resource usage. Specifically, message counts transmitted throughout the algorithm execution were recorded and compared across the different consensus methods. Figure 5 illustrates the trend of communication overhead changes for four consensus algorithms as the number of system nodes gradually increases. The \(x\)-axis represents the number of nodes in the network, while the \(y\)-axis denotes the communication overhead. Experimental results demonstrate that RE-PBFT consistently exhibits significantly lower communication overhead than other comparative algorithms across all node counts, reflecting higher communication efficiency. When the number of nodes is small (e.g., 10 or 20), the communication overhead differences among algorithms are minimal. However, as the node count increases, PBFT and RBFT show rapid upward trends in com

Figure 4: Throughput of Four Consensus Algorithms. munication overhead, with PBFT displaying the most pronounced growth. At 60 nodes, PBFT's communication overhead reaches 6480 instances, whereas RE-PBFT requires only 1880 instances--a reduction of approximately 70.99%. Similarly, RBFT and PPoR exhibit communication overheads of 4900 and 3550, respectively, at 60 nodes, compared with which RE-PBFT reduces communication load by 61.63% and 47.04%. These data indicate that RE-PBFT effectively decreases the frequency of message exchanges between nodes, significantly lowering system communication costs while maintaining consensus efficiency, with particularly notable advantages in large-scale network environments. The improved communication efficiency observed in RE-PBFT largely arises from refinements to the underlying consensus protocol, reducing communication redundancy in practical operations. Minimizing communication load is essential for large-scale consortium blockchain deployments, as it alleviates network congestion, reduces bandwidth demands, and enhances overall resource efficiency. All experimental data verify RE-PBFT's advantage in communication load performance. The reduced communication requirements make RE-PBFT especially suitable for applications emphasizing efficient network resource utilization. Overall experimental outcomes highlight that RE-PBFT demonstrates strong adaptability and reliability, particularly in highly dynamic network conditions or in scenarios requiring stringent system reliability, such as traceability applications in large-scale consortium environments. The node credit assessment mechanism incorporated within RE-PBFT comprehensively integrates factors like node performance, consensus participation stability, and node behavior, dynamically reflecting node trustworthiness. Consequently, RE-PBFT adeptly accommodates frequent changes in node states and sustains efficient consensus operations in fluctuating network conditions. Furthermore, the introduced node credit evaluation framework and decision tree classification algorithm effectively mitigate risks associated with Byzantine nodes, significantly bolstering system reliability and security. Figure 5: Communication overload of four consensus algorithms. ## 5 Conclusions

In this research, we introduced RE-PBFT, an enhanced consensus protocol designed with a sophisticated node credit assessment framework and an optimized decision-tree classification method.