@Article{cmc.2023.040970,
AUTHOR = {Yixia Chen, Mingwei Lin},
TITLE = {Linguistic Knowledge Representation in DPoS Consensus Scheme for Blockchain},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {77},
YEAR = {2023},
NUMBER = {1},
PAGES = {845--866},
URL = {http://www.techscience.com/cmc/v77n1/54506},
ISSN = {1546-2226},
ABSTRACT = {The consensus scheme is an essential component in the real blockchain environment. The Delegated Proof of Stake
(DPoS) is a competitive consensus scheme that can decrease energy costs, promote decentralization, and increase
efficiency, respectively. However, how to study the knowledge representation of the collective voting information
and then select delegates is a new open problem. To ensure the fairness and effectiveness of transactions in the
blockchain, in this paper, we propose a novel fine-grained knowledge representation method, which improves the
DPoS scheme based on the linguistic term set (LTS) and proportional hesitant fuzzy linguistic term set (PHFLTS).
To this end, the symmetrical LTS is used in this study to express the fine-grained voting options that can be chosen to
evaluate the blockchain nodes. PHFLTS is used to model the collective voting information on the voted blockchain
nodes by aggregating the voting information from other blockchain nodes. To rank the blockchain nodes and
then choose the delegate, a novel delegate selection algorithm is proposed based on the cumulative possibility
degree. Finally, the numerical examples are used to demonstrate the implementation process of the proposed DPoS
consensus algorithm and also its rationality. Moreover, the superiority of the proposed DPoS consensus algorithm is
verified. The results show that the proposed DPoS consensus algorithm shows better performance than the existing
DPoS consensus algorithms.},
DOI = {10.32604/cmc.2023.040970}
}