DT-PBFT: A Double-Layer Group Consensus Algorithm of Credibility for IoT Blockchain

Blockchain technology is becoming more and more popular, but performance problems have always troubled it, especially the scale of distributed networks is limited. Like Practical Byzantine consensus algorithm (PBFT), the main bottlenecks are concentrated in the scope of consensus too small, and the...

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Veröffentlicht in:2023 2nd International Conference on Big Data, Information and Computer Network (BDICN) S. 292 - 299
Hauptverfasser: Chen, Yaxin, Jia, Yongpu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.01.2023
Schlagworte:
blockchain
consensus
IoT
PBFT
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Zusammenfassung:Blockchain technology is becoming more and more popular, but performance problems have always troubled it, especially the scale of distributed networks is limited. Like Practical Byzantine consensus algorithm (PBFT), the main bottlenecks are concentrated in the scope of consensus too small, and the communication is too high. In order to implement PBFT in large-scale systems such as large-scale IoT ecosystems and blockchains, this paper proposes a reputation-based two-layer improved PBFT consensus computing model DT-PBFT. In order to better combine with the large -scale Internet of Things ecosystem, we give each node regional location, communication capacity, computing power and other related attributes to each involvement, so that Client nodes can input node attributes as parameters into the reputation calculation algorithm, to select the master node of each layer in the double-layer consensus model. Determining the master node through reputation improves the credibility of the consensus algorithm. At the same time, the DTPBFT cancels the Pre-Prepare stage, reducing communication complexity. Finally, this paper transforms the existing PBFT algorithm on the Fabric platform to implement the DT-PBFT consensus algorithm and compares its performance with traditional PBFT, proving that DT-PBFT has the best balance and excellent comprehensive performance in large-scale IoT application scenarios.
DOI:10.1109/BDICN58493.2023.00068