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A MCDM-based framework for blockchain consensus protocol selection

delete2022-10-01
delete20
PRE
AI
E
Ernestas Filatovas *
M
Marco Marcozzi
L
Leonardo Mostarda
R
Remigijus Paulavičius
DOI:10.1016/j.eswa.2022.117609delete
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摘要

摘要

En 中文
Blockchain is one of the most disruptive technologies introduced in Bitcoin, which engaged great attention from the industry and academia and determined a rapid growth of other Distributed Ledger Technologies (DLTs). In the complex architecture of a DLT system, a consensus protocol plays a key role by ensuring that all participants agree on the data integrity without any central authority. A wide range of consensus protocols have been designed with different concepts and properties (e.g., lower energy consumption, better scalability, smaller latency, higher throughput, etc.). The key requirements for consensus protocols passing from one blockchain system to another often differ significantly, and there is no one-fit-all protocol. Therefore, selecting the most suitable consensus protocol for a particular DLT system is essential, but at the same time a challenging step, as decision-makers need to make a trade-off between conflicting requirements. This paper introduces a framework for selecting the most suitable consensus protocols depending on the identified criteria, priorities, and other requirements by incorporating Multi-Criteria Decision-Making (MCDM) techniques. We demonstrate its potential by identifying the preferable consensuses for the three most common types of existing blockchain systems and on an actual application for bike renting. Moreover, the collected data and tools are freely available, ensuring full replicability, reusability, and further development.
Keyword:
Distributed Ledger Technology (DLT)
Blockchain
Consensus protocol selection
Multi-Criteria Decision-Making (MCDM)

期刊

Expert Systems with Applications 封面图
Expert Systems with Applications
IF:
7.5
论文数:
3.0W
被引数:
10.2W

机构

V
Vilnius University
学者数:
7.7K
论文数: 6.0K
被引数: 5.8K
U
University of Camerino
学者数:
4.2K
论文数: 3.5K
被引数: 2
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