A hierarchical byzantine fault tolerance consensus protocol for the Internet of Things

Rongxin Guo , Zhenping Guo , Zerui Lin , Wenxian Jiang

High-Confidence Computing ›› 2024, Vol. 4 ›› Issue (3) : 100196

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High-Confidence Computing ›› 2024, Vol. 4 ›› Issue (3) : 100196 DOI: 10.1016/j.hcc.2023.100196
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A hierarchical byzantine fault tolerance consensus protocol for the Internet of Things

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Abstract

The inefficiency of Consensus protocols is a significant impediment to blockchain and IoT convergence development. To solve the problems like inefficiency and poor dynamics of the Practical Byzantine Fault Tolerance (PBFT) in IoT scenarios, a hierarchical consensus protocol called DCBFT is proposed. Above all, we propose an improved k-sums algorithm to build a two-level consensus cluster, achieving an hierarchical management for IoT devices. Next, A scalable two-level consensus protocol is proposed, which uses a multi-primary node mechanism to solve the single-point-of-failure problem. In addition, a data synchronization process is introduced to ensure the consistency of block data after view changes. Finally, A dynamic reputation evaluation model is introduced to update the nodes’ reputation values and complete the rotation of consensus nodes at the end of each consensus round. The experimental results show that DCBFT has a more robust dynamic and higher consensus efficiency. Moreover, After running for some time, the performance of DCBFT shows some improvement.

Keywords

IoT / Hierarchical consensus / Clustering / Reputation evaluation model / Practical byzantine fault tolerance

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Rongxin Guo, Zhenping Guo, Zerui Lin, Wenxian Jiang. A hierarchical byzantine fault tolerance consensus protocol for the Internet of Things. High-Confidence Computing, 2024, 4(3): 100196 DOI:10.1016/j.hcc.2023.100196

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Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This work was supported by the Science and Technology Plan Project of Quanzhou City, Fujian Province of China (2022C020R) and the Science and Technology Plan Project of Fujian Province of China (2023H0012).

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