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LC-PBFT: Layered cross-chain consensus algorithm based on forest topology.

Authors :
Li, Jinhui
Cao, Lifeng
Zhao, Shoucai
Wan, Jiling
Bai, Jinlong
Source :
Journal of Supercomputing. Aug2024, Vol. 80 Issue 12, p17849-17873. 25p.
Publication Year :
2024

Abstract

As the core of blockchain technology, the consensus mechanism ensures the safe and stable operation of the blockchain system. According to the more complex traceability of data when sharing cross-system and cross-chain, the paper proposes the Layered Cross-Chain Practical Byzantine Fault Tolerance (LC-PBFT) suitable for cross-chain scenarios to ensure the secure traceability of data in the process of cross-chain data traceability. Firstly, in a single chain, the initial reputation value is set according to the success rate and efficiency of the nodes in the historical transactions in the chain, the reputation ranking of the nodes in the chain is realized by the improved PageRank algorithm, and delegated nodes are selected to participate in the cross-chain consensus based on reputation ranking. Then, the forest topology is sorted according to the contribution of the consensus nodes in the cross-chain system, and the consensus results from the child nodes are quickly verified by aggregation signature, which prevents the parent node from tampering with the consensus results of the child nodes. Meanwhile, the consensus process between the sibling nodes in the same layer and the view switching protocol when the master node fails are given. Finally, the performance of the LC-PBFT consensus algorithm is analyzed, and its advantages in performance such as throughput are verified by simulation. Compared with the Practical Byzantine Fault Tolerance (PBFT) algorithm, the consensus latency of the LC-PBFT consensus algorithm is reduced by 50.17%, and the throughput is improved by 19.37%. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09208542
Volume :
80
Issue :
12
Database :
Academic Search Index
Journal :
Journal of Supercomputing
Publication Type :
Academic Journal
Accession number :
178339408
Full Text :
https://doi.org/10.1007/s11227-024-06122-9