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This paper proposed a credit-score-based Byzantine fault-tolerant consensus algorithm, called CS-Raft, to address the inadequate performance of the current consensus algorithms in consortium chains. Initially, it assigned credit score attributes to all nodes and updated the credit score of each node according to their consensus behavior, activity level, time of joining the cluster and other metrics. The higher the credit score, the higher the node's trustworthiness. Secondly, it selected monitoring nodes based on their credit scores, which had the authority to inspect and participate in leader elections. The setting of monitoring nodes could effectively resist attacks from Byzantine malicious nodes. Finally, it made improvements to address the issue of vote splitting in leader elections, thereby enhancing the speed of leader election. Experimental analysis shows that compared to the PBFT algorithm, the CS-Raft algorithm effectively reduces consensus latency, increases system throughput, and accelerates leader election speed while achieving Byzantine fault tolerance. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the existing Byzantine fault-tolerant consensus mechanism applied to alliance chain, such as insufficient scalability and difficulty to support efficient and secure consensus under large-scale network nodes, this paper proposed a practical Byzantine fault-tolerant consensus algorithm(called H-PBFT) based on Raft clustering grouping by node grouping strategy. Firstly, the shuffled frog leaping algorithm combined with K-medoids clustering strategy using to cluster nodes in the system into multiple groups participated. The central nodes of each cluster nodes in the PBFT consensus of the main consensus cluster and the Raft algorithm introduced the improved supervision node was used to achieve consensus in each cluster group. Intra-group consensus mechanism supervised the introduction of nodes to make Raft algorithm Byzantine-resistant. Experimental results show that, compared with PBFT and Raft, this algorithm can improve fault tolerance performance in large-scale network node environment, and can also cluster nodes quickly and efficiently, improve consensus efficiency, reduce consensus communication overhead and complexity. This algorithm has better scalability, which can be better applied in alliance chain scenarios. [ABSTRACT FROM AUTHOR]

Knowledge graph embedding designs to map the entities and relations into low-dimensional and dense vector spaces. The existing embedded models still have the following two defects: most of the existing models only focus on the semantic information of knowledge graph, but ignore plentiful hidden information of triples; the existing models only concern the unidirectional information of the entity, but ignore the bidirectional potential information. This paper proposed the knowledge graph embedding model Bi-HTGAT to solve these problems. The model designed a hierarchical type attention mechanism, considered the contribution of different entities of each type to the central entity under different relations. At the same time, this paper introduced the directional attention mechanism of the relations, fused the neighbor information in different directions to update the entities and relations embedding, and finally aggregated the two parts of information to obtain the final embedding of the entity. The experimental results show that Bi-HTGAT performs better than other baseline models in link prediction, which fully proves that Bi-HTGAT can further improve the accuracy of embedding results. [ABSTRACT FROM AUTHOR]

Due to the strong privacy of industrial Internet of Things( IloT) data, safely storing the data has become a key issue in the development of IloT. This paper proposed a model of IloT data storage based on consortium blockchain to solve this problem. In order to ensure the efficiency and security of data storage, firstly, the method built a node status credit evaluation model based on the original PBFT algorithm and added a voting mechanism. Secondly, combined with node status, it set multiple key generation centers in SM9 digital signature algorithm to jointly participate in key generation. This paper experimentally analyzed the throughput, consensus delay and communication overhead of the model. The experimental results show that compared with the traditional model, the proposed model further guarantees the security in terms of data storage, reducing hacker attacks and preventing data leakage or tampering, and increases the communication overhead by 40%. [ABSTRACT FROM AUTHOR]