IET Blockchain最新文献

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.

This research presents a blockchain-based framework for secure and efficient medical image sharing, prioritizing data integrity and privacy. The framework involves two key phases: image compression with feature extraction and image encryption with storage on the InterPlanetary File System (IPFS). Medical images are compressed using the JPEG algorithm to reduce file size while maintaining diagnostic value. A deep neural network-based subject sensitive hashing (SSH) algorithm ensures feature map integrity by extracting consistent features from both original and compressed images. Encrypted images, along with SSH-generated hashes, are securely stored in the IPFS server. The encryption key and hash sequence are used for secure image retrieval, with smart contracts validating access requests based on the hash sequence. This multi-stage feature extraction approach demonstrates robust image integrity, security, and privacy, as verified by experimental results. Achieving an average correctness rate of 98% across multiple datasets, the framework significantly enhances healthcare data management by addressing the challenges of secure, scalable, and private medical image sharing. This research contributes to the development of more efficient, reliable, and privacy-conscious solutions for medical image handling in healthcare systems.

This study aims to compare the financial performance of 13 companies from the 2021 Forbes blockchain 50, all of which are also featured in the Brand Finance Global 500 for their high brand value, excluding firms in the banking and insurance sectors. The Forbes blockchain 50 list was chosen because it identifies leading firms actively leveraging blockchain technology (BT), making it a relevant source for examining the relationship between blockchain use and financial performance. The financial performance of these companies was assessed using the TOPSIS multi-criteria decision-making method, with performance scores derived from profitability ratios. Results showed that companies in the technology, logistics and commercial services sectors achieved higher performance index scores compared to others. The findings demonstrate that technology companies utilising BT and possessing high brand values showed elevated performance index scores. These results suggest that the adoption of BT has a favourable impact on the financial performance and brand value of companies. This study highlights the strategic importance of BT and its role in shaping the future of financial performance and brand value across various sectors.

In the rapidly evolving landscape of healthcare research, the demand for medical data has become indispensably vital. Additionally, incentivising owners to share their health data while maintaining privacy and security is a key challenge. By leveraging the potential of blockchain technology, a solution can be developed to tackle these challenges. This paper presents a novel framework to enable secure medical data sharing and monetisation that utilises the inherent features of blockchain to establish a robust and transparent framework. The blockchain ensures tamper-proof storage, transparent data transactions, enhancing trust and data integrity. The framework's privacy-preserving features enable data owners to securely contribute and access medical data while maintaining control over their information. Furthermore, the framework introduces a unique monetisation mechanism for sharing data by incorporating stablecoin. Smart contracts and encryption techniques enhance data security and enforce consent-based data usage. The framework has the potential to revolutionise medical research, driving advancements in diagnosis, treatment, and public health. Finally, the experimental evaluation, analysis and comparison with the state-of-the-art-works prove the efficiency of the framework.

In this paper, a methodology for optimizing the proof-of-work (PoW) blockchain technology based on dynamic node clustering to reduce transaction time is proposed. A blockchain network modeling system that implements the approach of dynamic node distribution into clusters, allowing the system to be divided into subsystems was developed and implemented. An alternative consensus concept for more efficient interaction between network nodes was proposed. The results of the study demonstrate that the cluster structure improves network performance: it increases the number of transactions per second by 0.02 transactions, reduces the average transaction time by 1.67 s, increases throughput by 0.2 transactions, reduces latency by 1.667 s, and significantly reduces the overall energy consumption of the system (reduction by 5122 energy units). These data confirm the potential of the proposed approach for a wide range of practical applications.

The Payment Channel Network is widely recognized as one of the most effective solutions for handling off-chain transactions and addressing blockchain scalability challenges. In the Lightning Network, a popular off-chain mechanism, multi-hop payments are facilitated through Hashed Time-Locked Contracts (HTLCs). However, despite its broad adoption, HTLCs are susceptible to various attacks, such as Fakey, Griefing, and Wormhole attacks. In these attacks, adversaries aim to disrupt transaction throughput by exhausting channel capacity or stealing routing fees from honest nodes along the payment path. We propose a scheme called CommTLC, which leverages Pedersen commitments and signatures to detect and punish/prevent adversaries in Fakey, Griefing and Wormhole attacks. We implement the proposed scheme and analyse its security within the universal composability (UC) framework. Additionally, we compare the performance of CommTLC with the latest schemes, MAPPCN-OR and EAMHL+. The results demonstrate that CommTLC outperforms both MAPPCN-OR and EAMHL+ in communication overhead, with only a slight increase in computational overhead compared to MAPPCN-OR. Furthermore, the adversary detection time for Fakey, Griefing and Wormhole attacks using CommTLC is reduced to just a few milliseconds—specifically, less than 112 ms for a payment path involving five users.