Traditional intellectual property authentication relies on centralized intermediaries, which makes it difficult to address issues such as forgery, lack of trust, and opaque information. Combined with the characteristi...
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Traditional intellectual property authentication relies on centralized intermediaries, which makes it difficult to address issues such as forgery, lack of trust, and opaque information. Combined with the characteristics of blockchain, such as decentralization, tampering, and traceability, these challenges can be effectively dealt with. Aiming at the shortcomings of traditional consensus algorithms in intellectual property authentication, such as high communication overhead and low efficiency, the improved pbft (Practical Byzantine Fault Tolerance) algorithm (MBFT algorithm) is proposed and combined with the distributed database IPFS (Inter Planetary File System) to alleviate the pressure of blockchain data storage and enhance operational efficiency. The algorithm first adopts the evaluation system in the hierarchical mechanism, invokes the Fibonacci series incremental law to update the Score value of the nodes and sort them, and divides the nodes into the classification consensus layer, the consensus confirmation layer, and the supervision layer. Secondly, the Maglev algorithm is used to generate a lookup table and design a classification consensus strategy, which is divided into four consensus groups based on the characteristics of intellectual property categories, namely, the patent authentication consensus group, the trademark authentication consensus group, the copyright authentication consensus group, and the other types of authentication consensus group. Then, the algorithm optimizes the consistency protocol, carries out pbft consensus once in each of the classification consensus layers and consensus confirmation layers, according to the consensus situation, and realizes the nodes' dynamic update to ensure the consensus's accuracy and reliability. The experiments show that the MBFT algorithm performs better in terms of communication complexity and throughput. As the number and size of files increase, the execution time of IPFS progressively lengthens. However,
With the introduction of edge computing into the field of Internet of Things (IoT), the Cognitive Internet of Things (CIoT) has emerged as the next-generation solution for trust and intelligent reasoning in the IoT. T...
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With the introduction of edge computing into the field of Internet of Things (IoT), the Cognitive Internet of Things (CIoT) has emerged as the next-generation solution for trust and intelligent reasoning in the IoT. That also puts blockchain, with its unique consensus mechanism, transparency and trustworthiness, on the stage of IoT applications. At present, not much research is focused on blockchain's application in CIoT, whose development is to a large extent restricted by the inefficiency of the consensus algorithm. Considering the characteristics of CIoT, a multi-stage consensus algorithm of EIoT-pbft is proposed on the basis of pbft algorithm, which includes the Grouping stage, Scoring stage and Consensus reaching stage. EIoT-pbft meets the IoT edge computing setup by adopting a two-phase improved pbft algorithm and a scoring mechanism based on both location and reputation, thus achieving a great increase in consensus efficiency. Evaluation results show that EIoT-pbft takes 36.4% less time than pbft for a single consensus, and the performance remains stable over the 2500 node configurations we set up. Moreover, at a scale of 1000 nodes, the number of edge nodes to be configured to reduce the number of system communications by 90% compared to the pbft algorithm is only 5, making blockchain more customized for CIoT settings.
Blockchain-based healthcare IoT technology research enhances security for smart healthcare services such as real-time monitoring and remote disease diagnosis. To incentivize positive behavior among participants within...
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Blockchain-based healthcare IoT technology research enhances security for smart healthcare services such as real-time monitoring and remote disease diagnosis. To incentivize positive behavior among participants within a blockchain-based smart healthcare system, existing efforts employ benefit distribution and reputation assessment methods to enhance performance. Yet, there remains a significant gap in multidimensional assessment strategies and consensus improvements in addressing complex healthcare scenarios. In this paper, we propose a blockchain and trusted reputation assessment-based incentive mechanism for healthcare services (BtRaI). BtRaI provides a realistic and comprehensive reputation assessment with feedback to motivate blockchain consensus node participation, thus effectively defending against malicious behavior in the healthcare service system. Specifically, BtRaI first introduces multiple moderation factors for comprehensive multidimensional reputation assessment and credibly records the assessment results on the blockchain. Then, we propose an improved pbft algorithm, grounded in the reputation assessment, to augment blockchain consensus efficiency. Finally, BtRaI designs a token-based reward and punishment mechanism to motivate honest participation in the blockchain, inhibit potential misbehavior, and promote enhanced service quality in the healthcare system. Theoretical analysis and simulation experiments conducted across various scenarios demonstrate that BtRaI effectively suppresses malicious attacks in healthcare services, improves blockchain node fault tolerance rates, and achieves blockchain transaction processing efficiency within 0.5 s in a 100-node consortium chain. BtRaI's reputation assessment and token incentive mechanism, characterized by realistic differentiation granularity and change curves, are well-suited for dynamic and complex healthcare service environments.
With the development and popularity of the Internet, blockchain technology is gradually integrated into the teaching of film and television courses in higher education institutions, and how to use blockchain technolog...
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With the development and popularity of the Internet, blockchain technology is gradually integrated into the teaching of film and television courses in higher education institutions, and how to use blockchain technology for teaching has become a new research hotspot. This paper takes pbft algorithm as the theoretical basis and constructs pbft-blockchain model. The teaching situation of film and television courses and the teaching path of film and television courses are studied in depth. The calculation results show that the number of traditional courses is relatively single, and the satisfaction of teachers and students is only 15.2%. The calculation result of film and television resources is "scarce". The satisfaction rate of students and teachers was only 18.3%. The highest satisfaction rate of students and teachers is the length of teaching, which is 41.4%. It is calculated that the weight of teaching strategy in the teaching path is 67%, and the proportion of teaching results under this weight is 78%. The weight of teaching format in the teaching path is 58%, and the corresponding teaching outcomes under this weight is 84%. The highest weight in the teaching path is the teaching session. Its weight is 83%, and the teaching outcome is 91%. Through the pbft-blockchain teaching model, two designs of teaching sessions are proposed: problem/event-initiated teaching and material-initiated teaching.
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