Enhanced Practical Byzantine Fault Tolerance via Dynamic Hierarchy Management and Location-Based Clustering.

Gwangyong Kim, Jinsung Cho, Min Choi, Bongjae Kim

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Gwangyong KimDepartment of Computer Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.ORCID 0009-0008-1904-7374

Jinsung ChoHuman IT Convergence Research Center, Korea Electronics Technology Institute, Seongnam 13509, Republic of Korea.ORCID 0000-0002-9771-5672

Min ChoiSchool of Information and Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.ORCID 0000-0002-8031-1022

Bongjae KimDepartment of Computer Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.ORCID 0000-0002-4310-6687

Funding

National Research Foundation of Korea NRF-2017R1E1A1A01075128

6 · The paper itself

Abstract

Blockchain is a distributed database technology that operates in a P2P network and is used in various domains. Depending on its structure, blockchain can be classified into types such as public and private. A consensus algorithm is essential in blockchain, and various consensus algorithms have been applied. In particular, a non-competitive consensus algorithm called PBFT is mainly used in private blockchains. However, there are limitations to scalability. This paper proposes an enhanced PBFT with dynamic hierarchy management and location-based clustering to overcome these problems. The proposed method clusters nodes based on location information and adjusts the dynamic hierarchy to optimize consensus latency. As a result of the experiment, the proposed PBFT showed significant performance improvement compared to the existing typical PBFT and Dynamic Layer Management PBFT (DLM-PBFT). The proposed PBFT method showed a processing performance improvement rate of approximately 107% to 128% compared to PBFT, and 11% to 99% compared to DLM-PBFT.

Indexed as

blockchainconsensus algorithmdynamic hierarchy managementlocation-based clusteringPBFTscalability

Identifiers

PMID38202922

DOI10.3390/s24010060

PMCPMC10780467

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.