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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v10i1.12840</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>NI-HotStuff: A Reputation-Driven Committee Framework for Efficient and Robust BFT Consensus</title><url>https://artdesignp.com/journal/JERA/10/1/10.26689/jera.v10i1.12840</url><author>ZhengLong</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>10</volume><issue>1</issue><history><date date-type="pub"><published-time>2026-02-12</published-time></date></history><abstract>Consensus mechanisms are fundamental to blockchain systems, ensuring that distributed nodes agree on the validity of transactions and data. However, performance bottlenecks, particularly those related to throughput, latency, and node selection, have increasingly constrained the scalability of modern blockchain deployments. To address these issues, this paper proposes NI-HotStuff, a reputation-driven committee-based BFT consensus framework built upon the HotStuff protocol. A CatBoost-based reputation model is introduced to learn and evaluate historical behavioral features of nodes, enabling quantitative reputation scoring. A hardware-aware bidding mechanism is further incorporated to dynamically compute each node’s bid value and integrate it with its reputation score, thereby prioritizing stable and high-performance nodes for consensus participation. Moreover, a committee mechanism is established in which a set of &amp;nbsp;committee nodes were selected from the candidate pool, and only committee members participate in the consensus process, reducing redundant communication and mitigating the performance drag caused by weak nodes. On top of that, a leader-selection strategy based on reputation values and inter-view time intervals is designed to prevent low-reputation or potentially malicious nodes from frequently becoming leaders. Experimental results demonstrate that NI-HotStuff significantly outperforms traditional PBFT and HotStuff in terms of communication overhead, consensus latency, and system throughput, with particularly notable improvements in small- and medium-scale node environments.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Nakamoto S, Bit B, 2008, Bitcoin: A Peer-to-Peer Electronic Cash System. 2008, 2007.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Yuan F, Zuo Z, Jiang Y, et al., 2015, AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection. Algorithms, 18(5): 263.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Amiri M, Wu C, Agrawal D, et al., 2024, The Bedrock of Byzantine Fault Tolerance: A Unified Platform for BFT Protocols Analysis, Implementation, and Experimentation. 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24), 371–400.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Yuan F, Huang X, Zheng L, et al., 2025, The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains. Information, 16(4): 268.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Castro M, Liskov B, 1999, Practical Byzantine Fault Tolerance. OsDI, 99(1999): 173–186.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B6" content-type="article"><label>6</label><element-citation publication-type="journal"><p>Yin M, Malkhi D, Reiter M, et al., 2019, HotStuff: BFT Consensus with Linearity and Responsiveness. Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, 2019: 347–356.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B7" content-type="article"><label>7</label><element-citation publication-type="journal"><p>Malkhi D, Yin M, 2023, Lessons from HotStuff. Proceedings of the 5th Workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating Algorithms for Distributed Systems, 2023: 1–8.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B8" content-type="article"><label>8</label><element-citation publication-type="journal"><p>Jalalzai M, Niu J, Feng C, et al., 2023, Fast-Hotstuff: A Fast and Robust BFT Protocol for Blockchains. IEEE Transactions on Dependable and Secure Computing, 21(4): 2478–2493.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B9" content-type="article"><label>9</label><element-citation publication-type="journal"><p>Abraham I, Malkhi D, Nayak K, et al., 2020, Sync Hotstuff: Simple and Practical Synchronous State Machine Replication. 2020 IEEE Symposium on Security and Privacy (SP). IEEE, 2020: 106–118.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B10" content-type="article"><label>10</label><element-citation publication-type="journal"><p>Liang B, Yuan F, Deng J, et al., 2025, Cs-pbft: A Comprehensive Scoring-Based Practical Byzantine Fault Tolerance Consensus Algorithm. The Journal of Supercomputing, 81(7): 859.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B11" content-type="article"><label>11</label><element-citation publication-type="journal"><p>Lamport L, Shostak R, Pease M, 2019, The Byzantine Generals Problem, Concurrency: The Works of Leslie Lamport, 203–226.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B12" content-type="article"><label>12</label><element-citation publication-type="journal"><p>Zhang Z, Hu B, Tian L, et al., 2025, Efficient Dynamic-Committee BFT Consensus Based on HotStuff. Peer-to-Peer Networking and Applications, 18(3): 111.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B13" content-type="article"><label>13</label><element-citation publication-type="journal"><p>Prokhorenkova L, Gusev G, Vorobev A, et al., 2018, CatBoost: Unbiased Boosting with Categorical Features. Advances in Neural Information Processing Systems, 2018: 31.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B14" content-type="article"><label>14</label><element-citation publication-type="journal"><p>Breskuvienė D, Dzemyda G, 2023, Categorical Feature Encoding Techniques for Improved Classifier Performance When Dealing with Imbalanced Data of Fraudulent Transactions. International Journal of Computers Communications &amp; Control, 18(3).</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B15" content-type="article"><label>15</label><element-citation publication-type="journal"><p>Wang Z, Chen L, Wang F, 2023, Fuzzy Inference Attention Module for Unsupervised Domain Adaptation. IEEE Transactions on Fuzzy Systems, 32(4): 1706–1718.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B16" content-type="article"><label>16</label><element-citation publication-type="journal"><p>Wang Z, Wang X, Liu F, et al., 2021, Adaptative Balanced Distribution for Domain Adaptation with Strong Alignment. IEEE Access, 2021(9): 100665–100676.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B17" content-type="article"><label>17</label><element-citation publication-type="journal"><p>Ou W, Chen B, Dai X, et al., 2023, A Survey on Bid Optimization in Real-Time Bidding Display Advertising. ACM Transactions on Knowledge Discovery from Data, 18(3): 1–31.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B18" content-type="article"><label>18</label><element-citation publication-type="journal"><p>PankiRaj J, Yassine A, Choudhury S, 2019, An Auction Mechanism for Profit Maximization of Peer-to-Peer Energy Trading in Smart Grids. Procedia Computer Science, 2019(151): 361–368.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B19" content-type="article"><label>19</label><element-citation publication-type="journal"><p>Malik S, Thakur S, Duffy M, et al., 2023, Comparative Double Auction Approach for Peer-to-Peer Energy Trading on Multiple Microgrids. Smart Grids and Sustainable Energy, 8(4): 21.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B20" content-type="article"><label>20</label><element-citation publication-type="journal"><p>Dramitinos M, Stamoulis G, Courcoubetis C, 2007, An Auction Mechanism for Allocating the Bandwidth of Networks to their Users. Computer Networks, 51(18): 4979–4996.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B21" content-type="article"><label>21</label><element-citation publication-type="journal"><p>Yin M, Malkhi D, Reiter M, et al., 2018, HotStuff: BFT Consensus in the Lens of Blockchain, arXiv, https://doi.org/10.48550/arXiv.1803.05069</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
