IET Blockchain最新文献

The rapid development of educational informatisation has made smart classrooms a key platform for advancing innovative teaching methods. However, traditional evaluation systems face challenges related to data security, fairness, and result traceability. This paper introduces a blockchain-based teaching evaluation system for smart classrooms to address these issues and improve teaching quality and management efficiency. The system employs a cloud-network-edge-device architecture, integrating cloud computing, network communication, and edge devices for real-time data collection, secure transmission, and intuitive visualisation. Blockchain technology ensures data integrity and transparency, while the practical Byzantine fault tolerance consensus algorithm maintains system reliability and prevents data manipulation. Experiments conducted at Dalian Jiaotong University demonstrate that the smart classroom improves teaching quality by 20% compared to traditional classrooms. The system is particularly effective in enhancing teaching resources and real-time communication, though improvements in student engagement are still needed. System performance tests indicate that the platform maintains low response times and stability under varying levels of concurrent requests, demonstrating its capability to support high-demand teaching scenarios and ensure data consistency and transparency.

The dramatic fluctuations of cryptocurrency prices in recent years have led to various studies on price forecasting, among which transformer-based forecasting methods have better prediction results on time series data. However, a large number of studies in the past have shown that transformer has the disadvantages of high computational cost and inability to predict global information and does not consider external information. To solve these problems, we propose an adaptive decomposition method based on transformer, which combines transformer with seasonal trends and external factors, i.e. the global information of the time series obtained by the decomposition method through adaptation is combined with external information. We conducted experiments on different datasets, and the results show that our method can significantly improve the performance of the baseline model.

Regulatory frameworks play a large part in the effectiveness of blockchain adoption. This study focused on determining the regulatory policies that promote the growth of blockchain adoption and analysing the impact of these regulatory approaches on blockchain adoption in finance sectors in emerging markets. The study used a comparative policy analysis to compare policies and regulations from chosen emerging markets. The countries included in the analysis are China, India, Kenya, Nigeria, Albania and Turkey. The findings suggest a correlation between supportive policy environments, such as those in Turkey, Albania and India, and more advanced applications of blockchain technology; however, this relationship is likely influenced by additional factors, including economic capacity, institutional readiness and technological infrastructure. In contrast, countries with restrictive policies, such as Kenya and Nigeria, ranked lower in blockchain adoption in the finance sector. Accordingly, a unified approach to blockchain regulations and cooperation between participating stakeholders is recommended. This study was limited in that only a few countries were chosen for analysis, which could have limited the scope of the comparative analysis.

This article presents algorithms and smart contract designs to facilitate recurring bill payments on decentralised platforms using the Ethereum-like Virtual Machine (EVM). The proposed approach enables the inclusion of recurring and deferred transactions, ensuring compatibility with ERC-20 and ERC-777 standards, thereby contributing to the establishment of a novel token standard. The research addresses the growing need for non-custodial mechanisms to support automatic periodic payments in decentralised environments, offering potential benefits to both service providers and customers in various industries.

Smart contracts are the cornerstone of decentralized applications and financial protocols, extending digital currency transactions but also introducing serious security challenges that cause substantial economic losses. Existing solutions primarily target code-level vulnerabilities, which constitute only a portion of all security incidents. Though prior research conducts static analysis across contract lifecycles, they lack the characteristic analysis of vulnerabilities in each stage and the distinction between the vulnerabilities. This paper presents the first empirical study on the security of smart contracts throughout their lifecycle, including deployment and execution, upgrade, and destruction stages. We propose two feature categories: transaction features (contract lifespan and four dynamic features: transaction numbers, transaction amounts, neighbour numbers, and the number of old and new neighbours) and ego network properties (temporal network density and local clustering coefficient). These seven features capture behavioural patterns across dimensions, including activity duration, transaction frequency, financial liquidity, interaction breadth, dynamic interaction changes, and structural properties of the transaction network. Finally, five machine learning models—logistic regression, random forest, support vector machine, decision tree, and K-nearest neighbours—are used to identify vulnerabilities at different stages. Results show that vulnerable contracts exhibit distinct transaction features and ego network properties at different lifecycle stages.

The convergence of blockchain and artificial intelligence (AI) has led to the emergence of AI-based tokens, which are cryptographic assets designed to power decentralized AI platforms and services. This paper provides a comprehensive review of leading AI-token projects, examining their technical architectures, token utilities, consensus mechanisms, and underlying business models. We explore how these tokens operate across various blockchain ecosystems and assess the extent to which they offer value beyond traditional centralized AI services. Based on this assessment, our analysis identifies several core limitations. From a technical perspective, many platforms depend extensively on off-chain computation, exhibit limited capabilities for on-chain intelligence, and encounter significant scalability challenges. From a business perspective, many models appear to replicate centralized AI service structures, simply adding token-based payment and governance layers without delivering truly novel value. In light of these challenges, we also examine emerging developments that may shape the next phase of decentralized AI systems. These include approaches for on-chain verification of AI outputs, blockchain-enabled federated learning, and more robust incentive frameworks. Collectively, while emerging innovations offer pathways to strengthen decentralized AI ecosystems, significant gaps remain between the promises and the realities of current AI-token implementations. Our findings contribute to a growing body of research at the intersection of AI and blockchain, highlighting the need for critical evaluation and more grounded approaches as the field continues to evolve.

Blockchain faces problems such as network congestion, high data propagation latency, and low throughput. These problems seriously affect the large-scale application of blockchain, so the performance optimization of blockchain networks is imminent. To reduce the propagation delay of transactions and blocks in the blockchain network and create conditions for shortening the block interval to increase throughput, we design a novel blockchain network structure and transaction propagation method. We propose a neighbouring node selection algorithm based on location and delay, introduce clusters and supernodes, and construct a low-latency network structure. In the new network structure, we design a transaction propagation method based on random linear network coding to reduce the latency and bandwidth consumption of syncing transactions across the entire network. The improvement in transaction propagation delay minimizes the probability of missing transactions in compact block, thus indirectly optimizing the block propagation delay. Simulation results show that our network structure can reduce block propagation delay by 19.7%. The new network structure combined with the transaction propagation method based on network coding enables the overall scheme to shorten the time it takes for a block to reach consensus in the network by 65.1%.

With the rapid development of Compute-First Networking (CFN), network computing resources have become a critical factor in network forwarding, while the centralization defects of traditional Internet trust systems—such as single authentication methods, vulnerability of central nodes, and low scalability—pose significant challenges to network security and transmission efficiency. Blockchain technology, characterized by tamper-proofing, distributed sharing, and decentralization, offers a novel solution to enhance trustworthiness in CFN. This study aims to construct a network path quality assurance and dynamic trust evaluation mechanism for CFN based on blockchain technology. The goal is to address the centralization issues of traditional systems, improve the reliability of computing resources in network forwarding, and verify the technical feasibility through experimental validation. In the system design phase, it develops blockchain data structures, implements smart contracts, and establishes a network quality monitoring mechanism; In the algorithm optimization phase, it employs a fuzzy algorithm for dynamic node deployment and uses mathematical models (Equations 1-3) to reduce latency and optimize transmission paths; In the experimental validation phase, it simulates CFN environments in laboratories to compare the performance of blockchain and traditional encrypted communication in terms of latency, bandwidth, and reliability. Experimental results demonstrate that blockchain technology enables more effective backtracking of network states and provides better forwarding paths in CFN environments.Experimental verification confirms that this technology achieves approximately 90% accuracy for network path verification protocols under attack scenarios, surpassing existing solutions, while simultaneously demonstrating superior performance in both latency and bandwidth metrics compared to conventional encryption protocols. This research confirms that blockchain technology effectively resolves centralization issues in traditional trust systems, providing a trustworthy mechanism for CFN network quality assurance. The findings offer technical support for next-generation Internet trust systems. Future work will focus on deploying the technology in real CFN environments and optimizing algorithms for practical applications.

As decentralised applications (dApps) and decentralised finance (DeFi) gain widespread adoption on Ethereum, scalability and transaction costs have emerged as critical challenges. Layer 2 (L2) rollups are promising solutions for improving scalability, but they suffer from significant gas costs due to the need for data availability on Ethereum Layer 1 (L1). Proto-Danksharding (EIP-4844) introduces blob transactions to reduce calldata costs, providing a new avenue for cost optimisation. This paper presents a comprehensive gas efficiency and economic analysis of EIP-4844's impact on Layer 2 rollups, providing mathematical models, simulation results, and architectural diagrams to illustrate the improvements in gas costs and system design.

Can virtual ownership redefine our digital economy? As online communities expand, a new digital economy is emerging, driven by Non-Fungible Tokens (NFTs). NFTs are unique digital assets that represent ownership of virtual items such as artwork, collectibles and gaming assets. However, the rapidly evolving NFT landscape presents several challenges. The present study investigates the transformative impact of NFTs on digital ownership and market dynamics while addressing the challenges and opportunities within virtual economies. The utilisation of NFTs is explored, focusing on aspects such as ownership, valuation, and market behaviour. A comprehensive literature review and analysis of marketplace data from Cryptoslam's API are conducted to uncover key trends and dynamics. Findings reveal that the NFT market is highly speculative; with value concentrated in a small number of high-priced NFTs. Aesthetic and emotional appeal significantly influence these valuations, often driven by hype rather than intrinsic value. Despite these challenges, NFTs foster immersive experiences and personalised identities, revolutionising digital ownership and commerce. This study underscores the need for innovation, interoperability, and robust governance to ensure the sustainable growth of the NFT ecosystem. By addressing market manipulation and regulatory concerns, NFTs can continue to shape a thriving digital economy.