JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jera.v10i2.13545ArticleRGS-PBFT: A Reputation-Based Grouping and Supervision PBFThttps://artdesignp.com/journal/JERA/10/2/10.26689/jera.v10i2.13545QiXiangyuan,QiangGuanglei,ZhangQianxiang,FanZhuokun,DuYu20261022026-03-31To address the problems of high communication overhead, insufficient robustness in master node election, and difficulty in timely management of faulty nodes in large-scale networks, the traditional PBFT consensus algorithm is proposed as a reputation-based grouping and supervision PBFT (RGS-PBFT). This method constructs a dynamic reputation score model based on the behavioral characteristics of nodes during the consensus process, such as verification correctness, voting participation, and operational stability. Based on this model, nodes are divided into consensus groups, candidate node groups, and ordinary node groups, enabling adaptive adjustment of node roles. During the master node election phase, the top 5% of nodes with the highest reputation scores from the consensus groups are selected as the candidate set. The ordinary node group and the candidate node group elect the master node through reputation-weighted voting, thus balancing the reliability of the master node with the decentralized nature of the election process. During the consensus process, only the consensus group participates in the core voting and confirmation process of PBFT to reduce communication complexity, while the remaining nodes mainly undertake verification and supervision responsibilities. Simultaneously, a monitoring and punishment mechanism for low-reputation nodes is introduced to limit, downgrade, and isolate faulty and abnormal nodes. Experimental results show that this mechanism, while maintaining Byzantine fault tolerance, can effectively reduce consensus communication overhead and improve master node election stability and overall system performance.

Wu X, Wang Z, Li X, et al., 2025, DBPBFT: A Hierarchical PBFT Consensus algorithm with dual blockchain for IoT. Future Generation Computer Systems, 2025(162): 107429.

Liu X, Fan X, Niu B, et al., 2025, 5G-Practical Byzantine Fault Tolerance: An Improved PBFT Consensus Algorithm for the 5G Network. Information, 16(3): 202.

Li W, Feng C, Zhang L, et al., 2020, A Scalable Multi-Layer PBFT Consensus for Blockchain. IEEE Transactions on Parallel and Distributed Systems, 32(5): 1146–1160.

Zhao P, Qiang G, Fan Y, et al., 2025, YOLO11-FGA: Express Package Quality Detection Based on Improved YOLO11. Information, 16(12): 1021.

Jiang W, Wu X, Song M, et al., 2023, Improved PBFT Algorithm Based on Comprehensive Evaluation Model. Applied Sciences, 13(2): 1117.

Liu J, Deng X, Li W, et al., 2024, CG-PBFT: An Efficient PBFT Algorithm Based on Credit Grouping. Journal of Cloud Computing, 13(1): 74.

Liu S, Zhang R, Liu C, et al., 2023, P-PBFT: An Improved Blockchain Algorithm to Support Large-Scale Pharmaceutical Traceability. Computers in Biology and Medicine, 2023(154): 106590.

Li Z, Wang J, Li Y, 2025, An Improved PBFT Consensus Algorithm Based on Reputation and Gaming: An Improved PBFT Consensus Algorithm. Journal of Supercomputing, 81(1).

Deng X, Zhang L, Zou Y, et al., 2025, DRAC‐PBFT: PBFT Consensus Optimisation Algorithm Based on Dynamic Reputation and Adaptive Clustering. IET Blockchain, 5(1): e70025.

Wang Y, Dou J, Yang P, et al., 2025, Improved PBFT Algorithm Based on Reputations Synchronization and Group Consensus. International Journal of High Speed Electronics and Systems, 34(3): 2440103.

Ding J, Wu X, Tian J, et al., 2025, RE-BPFT: An Improved PBFT Consensus Algorithm for Consortium Blockchain Based on Node Credibility and ID3-Based Classification. Applied Sciences, 15(13): 7591.

Hussain M, Mehmood A, Khan M, et al., 2025, Reputation-Based Leader Selection Consensus Algorithm with Rewards for Blockchain Technology. Computers, 14(1): 20.

Wang Y, Qiao Y, Ma J, et al., 2025, An Improved PBFT Consensus Algorithm for Blockchain Systems, Proceedings of the 9th International Conference on Electronic Information Technology and Computer Engineering, 715–719.

Zhang Q, Su J, Ma Z, et al., 2021, Blockchain Model Testing and Implementation Based on Improved PFBT Consensus. 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2021(2): 1010–1015.

Chen P, Chen Y, Wang X, et al., 2024, A High-Capacity Slicing PBFT Protocol Based on Reputation Evaluation Model. Wireless Networks, 30(9): 7469–7482.

Li Y, Wang Z, Fan J, et al., 2019, An Extensible Consensus Algorithm Based on PBFT. 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 17–23.