A Taxonomic Hierarchy of Blockchain Consensus Algorithms: An Evolutionary Phylogeny Approach

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 5; p. 2739
• Main Authors: Kim, Heesang, Kim, Dohoon
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.03.2023; MDPI
• Subjects: Algorithms; Analysis; Blockchain; Byzantine fault; Classification; Cluster Analysis; Clustering; Consensus; crash fault; Cryptography; Decision making; Digital currencies; distributed system; Domains; Evolutionary algorithms; Fault tolerance; Methods; Phylogeny; R&D; Research & development; Software; Taxonomy; Transition rules; Germany; consensus; crash fault; fault tolerance; taxonomic hierarchy; blockchain; distributed system; taxonomic tree; classification; Byzantine fault; taxonomic rank
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23052739

Countless endeavors have been undertaken to address the Byzantine Generals Problem, a generalization of the Two Generals Problem. The emergence of proof of work (PoW) for Bitcoin has led to various consensus algorithms diverging, and comparable existing consensus algorithms are being gradually utilized interchangeably, or only developed for each specific application domain. Our approach employs an evolutionary phylogeny method to classify blockchain consensus algorithms based on their historical development and current usage. To demonstrate the relatedness and lineage of distinct algorithms, as well as to support the recapitulation theory, which posits that the evolutionary history of its mainnets is mirrored in the development of an individual consensus algorithm, we present a taxonomy. We have created a comprehensive classification of past and present consensus algorithms that serves to organize this swift consensus algorithm evolution period. By recognizing similarities, we have compiled a list of different verified consensus algorithms and performed clustering on over 38 of these. Our new taxonomic tree presents five taxonomic ranks, including the evolutionary process and decision-making method, as a technique for analyzing correlation. Through the examination of the evolution and utilization of these algorithms, we have developed a systematic and hierarchical taxonomy that enables the grouping of consensus algorithms into distinct categories. The proposed method classifies various consensus algorithms according to taxonomic ranks and aims to reveal the direction of research on the application of blockchain consensus algorithms for each domain.