IET Blockchain最新文献

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.

Grid computing is an emerging technology that enables the heterogeneous collection of data and provision of services to users. Due to the high amount of incoming heterogeneous requests, grid computing needs efficient scheduling to reduce execution time and satisfy service level agreement (SLA) and quality of service (QoS) requirements. For that purpose, we proposed the SprakGrid method to reduce execution time and satisfy SLA, and QoS requirements. The proposed work includes four consecutive phases which are explained as follows, in first we perform user authentication to ensure the legitimacy of the users using the elliptic curve-based chaos theory algorithm which generates a secret key and stores it in the blockchain. In the second we perform query scheduling for resource discovery using the soft actor critic algorithm by considering 3P's parameters which is performed by spark environment that schedules optimal resources based on the service request. Third, we perform a risk assessment and request dropping, in which the risk nodes of workers are evaluated by the master node. To address the resource wastage by attackers, this research dynamically evaluates the risk value using Shannon entropy. Based on the risk assessment the requests are classified into two classes such as normal and malicious. Fourth we perform service live migration, in which the malicious requests are dropped and normal requests are migrated from the source node to the target node using multi-constraints based emperor penguin optimization. Finally, simulation is performed by GridSim and the simulation results demonstrate that the proposed SparkGrid method achieves superior performance compared to other state-of-the-art methods.

The production, distribution of the counterfeit drugs are the major issue in today's world. These drugs create a big economic crunch in today's economic world. The major concern now days the counterfeit of drug is increasing day by day. Counterfeit refers to the fake medicine which is now increasing. In 2020 where all the countries are suffering from counterfeit of medicine. The article discusses about how the secure transaction of drug can be made to the all suppliers. Blockchain is a distributed technology where all the transaction made is transparent. All the transaction made in blockchain will be stored with all the network that relate to the blockchain network. Blockchain contain the concept of hash function. Hash function works like linked list it contains the address of previous node and the next node also. Hash function contain the hexadecimal value. With the implementation of blockchain technology and hyperledger framework it can be able to detect the counterfeit of drug in the early stage. If it is found in the early stage the supply of counterfeit of drug in the pharmaceutical industry like hospitals, pharmacy shops can be stopped. To make the supply of drug secure the hyperledger framework of blockchain is implemented.

In the world of healthcare, joining machine learning with block chain tech offers a smart path for future predictions. The study aims on guessing what kinds of transactions happen in healthcare data stored on a block chain. Machine learning is used to sort these transactions right. This helps make healthcare tasks work on their own and do better. Health data is taken from block chains, looked at it closely, and ran various algorithms on it. Using features such as operation, date, and symbolic indicators, logistic regression, decision tree, random forest, and support vector machine are applied to classify transaction types. The decision tree algorithm achieved the highest accuracy at 89.29%, followed by random forest at 67.86%, logistic regression at 33.93%, and support vector machine at 39.29%. The findings demonstrate the effectiveness of machine learning in improving transaction classification within secure, decentralized medical data environments.