基于聚类算法的PBFT共识优化方案与验证
Optimization and Verification of PBFT Consensus Mechanism Using Clustering Algorithms

随着区块链技术在多个领域的广泛部署，其面临的共识机制的效率较低和计算资源的过度消耗的挑战。本研究通过对DBSCAN聚类算法进行改进，使之适应去中心化的区块链共识网络，进而对大规模的网络节点进行有效的聚类和层次化管理。通过引入了监督节点以增强共识模型的安全性。在模拟环境中进行实验，验证了通过适当的聚类可以显著优化共识过程中的耗时和通信复杂度。在拥有1,000个节点的网络环境中，相比于传统的PBFT算法，我们的方案能够将单次共识的耗时降低26.7%，并且在最佳情况下，共识通信的次数可以至少减少一个数量级。这一方案显著提高了参与共识节点的效率，使得区块链应用更加节能且高效。
As blockchain technology is extensively deployed across various sectors, it faces challenges associated with the inefficiency of consensus mechanisms and the excessive consumption of computational resources. This study improves the DBSCAN clustering algorithm to adapt it to the decentralized consensus networks of blockchain, thereby enabling effective clustering and hierarchical management of large-scale network nodes. Supervisory nodes are introduced to enhance the security of the consensus model. Experiments conducted in a simulated environment have verified that appropriate clustering can significantly optimize the time consumption and communication complexity during the consensus process. In a network environment with 1,000 nodes, compared to the traditional PBFT algorithm, our scheme can reduce the time required for a single consensus by 26.7%, and in the best-case scenario, the number of consensus communications can be reduced by at least an order of magnitude. This approach significantly improves the efficiency of nodes participating in the consensus, making blockchain applications more energy-efficient and effective.