Primary node election based on probabilistic linguistic term set with confidence interval in the PBFT consensus mechanism for blockchain

• Published in: Complex & intelligent systems Vol. 9; no. 2; pp. 1507 - 1524
• Main Authors: Xie, Mingyue, Liu, Jun, Chen, Shuyu, Xu, Guangxia, Lin, Mingwei
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.04.2023; Springer Nature B.V; Springer
• Subjects: Approximation; Attitudes; Blockchain; Complexity; Computational Intelligence; Confidence intervals; Cryptography; Data Structures and Information Theory; Decision making; Efficiency; Elections; Energy consumption; Engineering; Fault tolerance; Fuzzy sets; Intelligent systems; Linguistics; Nodes; Original Article; PBFT consensus mechanism; Primaries & caucuses; Primary node; Probabilistic linguistic term set; Semantics; Software engineering; Statistical analysis; TOPSIS; Voting; Probabilistic linguistic term set; PBFT consensus mechanism; TOPSIS; Blockchain; Primary node
• ISSN: 2199-4536; 2198-6053
• DOI: 10.1007/s40747-022-00857-9

This study proposes a primary node election method based on probabilistic linguistic term set (PLTS) for the practical Byzantine fault tolerance (PBFT) consensus mechanism to effectively enhance the efficiency of reaching consensus. Specifically, a novel concept of the probabilistic linguistic term set with a confidence interval (PLTS-CI) is presented to express the uncertain complex voting information of nodes during primary node election. Then, a novel score function based on the exponential semantic value and confidence approximation value for the PLTS-CI, called Score-ESCA, is used to solve the problems of comparing different nodes with various voting attitudes. This method helps select the node with the highest score by utilizing complex decision attitudes, making it an accurate primary node election solution. Furthermore, the feasibility of our proposed method is proved by both theoretical analysis and experimental evaluations.