ABSTRACT The transaction processing capacity of blockchain systems remains a critical barrier to adoption in real‐time applications. Recent studies have explored different optimization techniques, including sharding, off‐chain processing, and hybrid consensus algorithms. However, most of those techniques change the original architecture or process of the blockchain and may raise compatibility issues. Resolving these challenges calls for creative methods that can effectively balance transaction throughput with latency without compromising blockchains' core infrastructures. This paper proposes a learning to prediction framework combining a Kalman filter and artificial neural network for transaction throughput forecasting, integrated with a fuzzy logic controller embedded in smart contracts. The approach can dynamically optimize transaction traffic flow based on the predicted throughput and the observed transaction latency, thus improving blockchain performance in real‐time. Deployed on a hyperledger fabric healthcare testbed and evaluated through a series of ablation experiments, the results demonstrate a significant improvement over the baseline and therefore illustrate the potential of the proposed approach in improving blockchain performance for practical applications.

With the rapid development of Internet of Things (IoT) technology, interconnectivity between devices has become increasingly widespread. However, traditional IoT security measures struggle to cope with increasingly complex security threats and cannot fully exploit the advantages of interconnectivity due to the limited computational resources of the devices. To address this, we propose a blockchain-based IoT security framework comprising wallet component, smart contract component, multilayer security component, and common component. This framework, designed for resource-constrained environments and embedded into cellular communication modules, enables multiend offloading of computational tasks and secure transmission for IoT devices. Experimental results show that the data processing capability of the decentralized network architecture based on this framework is improved by 115.06% compared to traditional methods, enhances the security and autonomy of IoT devices, and significantly strengthens the degree of IoT decentralization. This provides a valuable reference for designing next-generation IoT security architectures.

Abstract Patient enrollment, data sharing, and data privacy are enormous medical challenges for clinical trial studies. In recent years, blockchain technology has drawn the attention of various researchers and institutes. As a new and innovative distributed ledger technology, blockchain can be critical to addressing these challenges, thus making clinical research transparent and building public trust fairly and openly. However, the existing literature lacks a comprehensive survey on the adoption of blockchain in clinical trials. To fill the research void, this paper presents a punctilious taxonomy of blockchain technology in clinical trials according to the literature. This taxonomy comprises decentralized scenarios, decentralized practices, blockchain types, deployment methods, and consensus algorithms. The results show that blockchain technology can cover all aspects of the clinical trial study in a decentralized, secure, transparent manner. Besides, some open research challenges of blockchain are categorized into three groups: technical challenges, security challenges, and organizational challenges. Moreover, some recent blockchain projects, micro applications in clinical trials, and several research areas or technologies for future research and development are discussed.

Presently, blockchain technology has been widely applied in various application domains such as the Internet of Things (IoT), supply chain management, healthcare, etc. So far, there has been much confusion about whether blockchain performs with scale, and admittedly, a lack of information about best practices that can improve the performance and scale. This paper proposes a novel blockchain network construction methodology to improve the performance of Hyperledger Fabric. As a highly scalable permissioned blockchain platform, Hyperledger Fabric supports a wide range of enterprise use cases from finance to governance. A comprehensive evaluation is performed by observing various configurable network components that can affect the blockchain performance. To demonstrate the significance of the proposed methodology, we set up the experiment environment for the baseline and the test network using optimized parameters, respectively. The experimental results indicate that the test network's performance is enhanced effectively compared to the baseline in transaction throughput and transaction latency.