Grouped Multilayer Practical Byzantine Fault Tolerance Algorithm: A Practical Byzantine Fault Tolerance Consensus Algorithm Optimized for Digital Asset Trading Scenarios.

PBFT Raft blockchain digital asset packet consensus

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
01 Nov 2023
Historique:
received: 21 09 2023
revised: 23 10 2023
accepted: 30 10 2023
medline: 14 11 2023
pubmed: 14 11 2023
entrez: 14 11 2023
Statut: epublish

Résumé

Based on the practical Byzantine fault tolerance algorithm (PBFT), a grouped multilayer PBFT consensus algorithm (GM-PBFT) is proposed to be applied to digital asset transactions in view of the problems with excessive communication complexity and low consensus efficiency found in the current consensus mechanism for digital asset transactions. Firstly, the transaction nodes are grouped by type, and each group can handle different types of consensus requests at the same time, which improves the consensus efficiency as well as the accuracy of digital asset transactions. Second, the group develops techniques like validation, auditing, and re-election to enhance Byzantine fault tolerance by thwarting malicious node attacks. This supervisory mechanism is implemented through the Raft consensus algorithm. Finally, the consensus is stratified for the nodes in the group, and the consensus nodes in the upper layer recursively send consensus requests to the lower layer until the consensus request reaches the end layer to ensure the consistency of the block ledger in the group. Based on the results of the experiment, the approach may significantly outperform the PBFT consensus algorithm when it comes to accuracy, efficiency, and preserving the security and reliability of transactions in large-scale network node digital transaction situations.

Identifiants

pubmed: 37960601
pii: s23218903
doi: 10.3390/s23218903
pmc: PMC10649370
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Références

J Am Med Inform Assoc. 2019 May 1;26(5):462-478
pubmed: 30907419
Sensors (Basel). 2020 Mar 10;20(5):
pubmed: 32164220
Sci Rep. 2022 Nov 24;12(1):20286
pubmed: 36434035

Auteurs

Jian Liu (J)

School of Information and Communication Engineering, Hainan University, Haikou 570228, China.

Wenlong Feng (W)

School of Information and Communication Engineering, Hainan University, Haikou 570228, China.

Mengxing Huang (M)

School of Information and Communication Engineering, Hainan University, Haikou 570228, China.

Siling Feng (S)

School of Information and Communication Engineering, Hainan University, Haikou 570228, China.

Yu Zhang (Y)

School of Computer Science and Technology, Hainan University, Haikou 570228, China.

Classifications MeSH