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A Taxonomic Hierarchy of Blockchain Consensus Algorithms: An Evolutionary Phylogeny Approach.
1Department of Computer Science, Kyonggi University, Suwon-si 16227, Republic of Korea.
Sensors (Basel, Switzerland)
|March 11, 2023
Summary
This study introduces a novel taxonomy for blockchain consensus algorithms, classifying them by evolutionary history and usage. This helps organize the rapid development of consensus mechanisms and guides future research directions.
Area of Science:
- Computer Science
- Distributed Systems
- Blockchain Technology
Background:
- The Byzantine Generals Problem highlights challenges in distributed consensus.
- Proof of Work (PoW) spurred diverse blockchain consensus algorithms, often used interchangeably or domain-specifically.
- Existing classifications struggle to keep pace with rapid algorithm evolution.
Purpose of the Study:
- To develop a systematic taxonomy for blockchain consensus algorithms.
- To classify algorithms based on historical development and current usage.
- To reveal research trends in blockchain consensus algorithm applications.
Main Methods:
- Employed an evolutionary phylogeny method for algorithm classification.
- Developed a hierarchical taxonomy with five ranks, including evolutionary process and decision-making.
- Clustered over 38 distinct verified consensus algorithms.
Main Results:
- Presented a comprehensive classification of past and present consensus algorithms.
- Demonstrated relatedness and lineage of algorithms, supporting recapitulation theory.
- Organized the swift evolution of consensus algorithms into a structured taxonomy.
Conclusions:
- The proposed taxonomy provides a systematic framework for understanding blockchain consensus algorithm evolution.
- It aids in grouping algorithms and identifying research directions for specific domains.
- Facilitates analysis of correlations between algorithm evolution and decision-making methods.
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