Multi-Dimensional Consensus Node Trust Evaluation and Selection for Blockchain-Empowered Electric Measurement Data Traceability

Abstract

With the deployment of internet of things-based monitoring terminals, massive electric measurement data are transmitted and shared among different grid company sectors. Consortium blockchain based consensus process has been adopted to enhance data traceability. However, previous studies face challenges including single-dimensional indication and lack of dynamic trustworthy degree update mechanism. Therefore, this paper proposes a node trust evaluation and selection algorithm based on improved fuzzy Petri network (IFPN). Firstly, the multi-dimensional node trust evaluation and selection framework for practical Byzantine fault tolerance (PBFT)-empowered electric measurement data sharing is constructed. On this basis, IFPN is proposed to dynamically evaluate measurement data node trust, and nodes participating in the PBFT consensus process are selected based on the Top-N algorithm. Further, the trustworthy degree of IFPN is dynamically updated based on the fuzzy deviation to improve the accuracy. Finally, the superiority of the proposed algorithm has been verified through simulation.