IET Blockchain最新文献

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.

In today's rapidly evolving technological landscape, the healthcare industry is a pivotal sector that demands continuous innovation. Although extensive research on blockchain technology exists, the literature has predominantly emphasized technical aspects and specific applications within industries. However, a comprehensive exploration of blockchain's business and patient value in healthcare remains limited. This study aims to address this gap by conducting a systematic literature review using the PRISMA framework, analyzing highly cited articles from reputable journals. The findings highlight blockchain's potential to revolutionize patient care and optimize business practices within the healthcare sector. This research provides valuable insights for stakeholders, emphasizing the transformative power of blockchain technology to foster innovation, particularly in public healthcare institutions. The results are presented through a value chain analysis and a mindmap, offering a clear framework that can guide researchers and developers in considering the value of blockchain use cases from multiple perspectives.

Organizations are adopting technological innovations to transform payment systems due to challenges with traditional methods, such as slow speed and high fees. These challenges have prompted a shift towards blockchain-based cryptocurrencies. However, cryptocurrency adoption for payments remains limited, especially in Saudi Arabia. This study adapts the technology acceptance model to explore cryptocurrency adoption through the blockchain-based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA) model. Factors within the model are assessed using an experimental vignette-task methodology and surveys. A key component is an educational package, offering comprehensive materials to explain blockchain technology. The findings confirm the reliability of surveys. Most model factors are statistically significant in influencing users’ intention to use cryptocurrency. The study finds that perceived ease of use, perceived usefulness, and perceived trust significantly impact participants’ intentions. Additionally, low transaction fees and age are the most influential factors on the technology acceptance model's core constructs. Statistical analysis indicates that decentralization and anonymity were insignificant and thus excluded from the revised BCAP-SA model. These findings highlight the potential to enhance cryptocurrency adoption in Saudi Arabia. The study's insights can guide strategies to promote wider cryptocurrency usage in the region.

The aim of this study is to design and implement a system that allows centralized blockchain institutions to prove their solvency. This system ensures that institutions do not misappropriate user assets and enhances trust between users and institutions. The article introduces the Groth-16 zero-knowledge proof algorithm from ZK-SNARK (zero-knowledge succinct non-interactive argument of knowledge). The R1CS arithmetic circuit in the Groth-16 algorithm effectively guarantees the authenticity and tamper-resistance of the system's raw data sources. Additionally, it combines the use of Merkle Sum Trees and Sparse Merkle trees. The former enables users to perform distributed verification of solvency proofs, while the latter effectively hides the overall number of users. Finally, users verify the balances and the private key signatures of addresses in the institution's bulletin board. Together, these components form a comprehensive and distributed solvency proof solution. This solution is a pioneering solution in the field of blockchain solvency proofs and provides a secure, efficient, and privacy-preserving method for centralized cryptocurrency service providers or Web3 enterprise custodians. It effectively addresses the challenge of proving an institution's possession of sufficient reserves to cover user assets without compromising user privacy or disclosing the institution's scale.

Blockchain technology ensures accountability, transparency, and redundancy, but its reliance on public-key cryptography makes it vulnerable to quantum computing threats. This article addresses the urgent need for quantum-safe blockchain solutions by integrating post-quantum cryptography (PQC) into blockchain frameworks. Utilizing algorithms from the NIST PQC standardization process, it is aimed to fortify blockchain security and resilience, particularly for IoT and embedded systems. Despite the importance of PQC, its implementation in blockchain systems tailored for embedded environments remains underexplored. A quantum-secure blockchain architecture is proposed, evaluating various PQC primitives and optimizing transaction sizes through techniques such as public-key recovery for Falcon, achieving up to 17% reduction in transaction size. The analysis identifies Falcon-512 as the most suitable algorithm for quantum-secure blockchains in computer-based environments and XMSS as a viable but unsatisfactory stateful alternative. However, for embedded-based blockchains, Dilithium demonstrates a higher transactions-per-second (TPS) rate compared to Falcon, primarily due to Falcon's slower signing performance on ARM CPUs. This highlights the signing time as a critical limiting factor within embedded blockchains. Additionally, smart contract functionality is integrated, assessing the impact of PQC on smart contract authentication. The findings demonstrate the feasibility and practicality, paving the way for robust and future-proof IoT applications.

In the new era of adopting and managing new and robust technologies in banking, the use of blockchain technology has significantly transformed overall banking systems. To add new insights to the body of existing knowledge, the authors conducted a systematic review with bibliographic network mapping to identify and analyse the factors contributing to adopting blockchain in the banking industry. Following the latest protocols of the PRISMA flowchart, this study acknowledged 16 relevant publications from 2590 papers in the databases, namely Scopus, ScienceDirect, Web of Science, and IEEE Xplore. The bibliographic data were grouped and analysed using VOSviewer to create network visualization maps that included citation and co-citation, bibliographic coupling, co-authorship, and co-occurrence of terms. Subsequently, significant terms were identified through the analyses and compared with those found in the 16 relevant papers. The aggregate findings suggest that multiple influencing factors have been recognized and later categorized into three thematic drivers: transparency-driven security, collaborative interoperability, and organizational infrastructure. The current research provides valuable insights for policymakers, technologists, researchers, consultants, and practitioners of information systems by proposing a technological framework, which will aid in developing tailored strategies to facilitate the sustainable practice of blockchain in the banking industry to a wider extent.