IET Blockchain最新文献

Airport checked luggage entails specific requirements for speed, stability, and reliability. The issue of abnormal retention of checked luggage presents a significant challenge to aviation safety and transportation efficiency. Traditional luggage monitoring systems exhibit limitations in terms of accuracy and timeliness. To address this challenge, this paper proposes a real-time detection and alerting of luggage anomaly retention based on the YOLOv5 object detection model, leveraging visual algorithms. By eliminating cloud servers and deploying multiple edge servers to establish a private chain, images of anomalously retained luggage are encrypted and stored on the chain. Data users can verify the authenticity of accessed images through anti-tampering algorithms, ensuring the security of data transmission and storage. The deployment of edge computing servers can significantly reduce algorithm latency and enhance real-time performance. This solution employs computer vision technology and an edge computing framework to address the speed and stability of checked luggage transportation. Furthermore, blockchain technology greatly enhances system security during operation. A model trained on a sample set of 4600 images achieved a luggage recognition rate of 96.9% and an anomaly detection rate of 95.8% in simulated test videos.

In order to encourage participants to actively join the data sharing and to meet the distributed structure and privacy requirement in the medical consortium, the data-sharing strategy based on the master-slave multichain is presented in this paper. According to the different computing resources and the responsibility of participants, the adaptive Proof of Liveness and Quality consensus and hierarchical federated learning algorithm for master-slave multichain are proposed. Meanwhile, by quantifying the utility function and the optimization constraint of participants, this paper designs the cooperative incentive mechanism of medical consortium in multi-leader Stackelberg game to solve the optimal decision and pricing selection of the master-slave multichain. The simulation experiments show that the proposed methods can decrease the training loss and improve the parameter accuracy by MedMINST datasets, as well as reach the optimal equilibrium in selection and pricing strategy in the system, guaranteeing the fairness of profit distribution for participants in master-slave multichain.

Blockchain trilemma is a considerable obstacle for today's decentralized systems. It is hard to achieve a perfect balance among decentralization, security, and scalability. Many popular blockchain platforms sacrifice scalability to preserve decentralization and security, resulting in low speed, reduced throughput, and poor real-time performance (the time from transaction initiation to confirmation). Currently, there are several technologies, such as sharding, directed acyclic graph technology, sidechains, off-chain state channels etc., that aim to improve throughput and real-time performance. However, most of these solutions compromise the core feature of blockchain, which is decentralization, and introduce new security risks. In this paper, the authors propose a novel method, called MEchain, based on the Proof of Time Series Algorithm. MEchain consists of two models: the multi-chain model and the elastic-chain model. The authors’ experimental results show that these two models can enhance real-time performance and throughput to a higher level in the industry.

Metaverse is a new ecology that integrates the digital world with the physical world, generates a mirror image of the real world based on digital twin technology, and guarantees the fairness of the virtual world through the trusted mechanism of blockchain. Cross-chain communication technology is the key to realizing data circulation and collaborative processing between blockchains, which provides the communication foundation for the massive connection of blockchains in the metaverse. The current cross-chain communication mode is dominated by direct-connect routing, leading to network congestion and high propagation delay once the direct-connect link fails and cannot be recovered quickly. To optimize direct-connect routing, this paper proposed a cross-chain routing optimization strategy based on RON (Resilient Overlay Network), that is, Cross-Chain_RON, which firstly applies RON to reconstruct the direct-connect routing model, and then selects the optimal link through the shortest-path algorithm and policy routing, and combines with the RON performance database to improve the data transmission efficiency. Finally, the two communication modes were simulated by simulation tools. The experiments show that Cross-Chain_RON outperforms the direct-connect routing in terms of latency, rate of successful data transmission, and throughput in poor network environments, but at the expense of a certain amount of system overhead.

In recent years, the Metaverse has gained attention as a hub for technological revolution. However, its main platform suffers from issues like low-quality content and lackluster virtual environments, leading to subpar user experiences. Concerns arise from declining interest in NFTs and failed virtual real estate ventures, casting doubt on the Metaverse's future. Artificial intelligence generated content (AIGC) emerges as a key driver of Metaverse advancement, using AI to create digital content efficiently and affordably. AIGC also enables personalized content, enhancing the Metaverse. This paper examines the link between the Metaverse and AIGC, exploring AIGC's applications, underlying technologies, and future challenges. It reveals that while AIGC shows promise for improving the Metaverse, its technologies must better align with development needs to deliver immersive experiences.

Logistics supply chain (LSC), a chain structure that integrates and coordinates all logistics transactions, has become an essential component of the modern logistics industry. By using blockchain, trusted logistics services enable participants to effectively record and track transactions during the logistics process. Current blockchain-based LSC features distributed structure and data privacy requirements, hindering the supervision of logistics transactions. Leveraging emerging dual-blockchain architecture to separate logistics transactions from supervision is a promising direction. However, the dual-blockchain collaboration restricts supervision due to its cross-chain privacy and efficiency. To address these issues, a logistics supply chain supervision scheme based on dual-blockchain collaboration (DBC) is proposed. First, an independent supervision blockchain is constructed to balance the contradiction between distributed structure and supervision requirements. Second, two mechanisms are designed to enhance the privacy and performance of collaborative supervision. The hybrid access control mechanism enables fine-grained supervision for different participants, and the aggregated transaction verification method supports efficient collaboration for logistics transactions. Security analysis and performance evaluation demonstrate the feasibility of DBC in enhancing the security and supervision of logistics data on the dual-blockchain architecture. Experimental results show that the cross-chain supervision overhead of DBC is reduced to $��1�/�n�$ of the baseline schemes.

The focus of this review article is on the societal problems and end user acceptance of blockchain technology. The paper begins by outlining the importance of blockchain in modernizing trust and data management systems and highlighting its rapid spread across numerous industries. In-depth analysis of the adoption-influencing aspects is done, which also lists the advantages and typical end-user problems. It examines the privacy implications, restrictions on pseudonymity, and function of technologies that improve privacy, such as zero-knowledge proofs, while also exploring the legal and regulatory environment around blockchain, putting a focus on digital identity, intellectual property, and data ownership. It also evaluates blockchain security features, such as flaws and risks associated with smart contracts, discusses best practices for boosting security, discusses the societal effects of blockchain, and makes suggestions for legislators, companies, and scholars. The use of blockchain technology and its effects on privacy, rights, and security are discussed in real-world case studies as well.

Transport ticketing systems are crucial for enabling seamless, efficient, and sustainable mobility. However, traditional ticketing systems face limitations such as ticket fraud, lack of interoperability, and the inability to adapt to changes in the dynamic transport networks they issue tickets for. This paper presents new approaches to the system for ticketing ubiquity with blockchains (STUB), a novel smart transport ticketing solution that employs ontochains, a hybrid data structure combining blockchains and ontologies to form a type of distributed knowledge graph. STUB aims to address these limitations by providing a secure, transparent, and flexible platform for ticket issuance, validation, and management. We describe the key components and workflow of the STUB system, highlighting the use of transport network ontologies for modelling complex relationships within transportation systems and blockchain technologies for transport network ontology's state. Additionally, the implementation of Merkle proofs for efficient and secure validation between on-chain and off-chain ontological data is discussed. Finally, a simulated toy example is used as a lightweight proof-of-concept to demonstrate these capabilities. The proposed STUB system has the potential to significantly impact the future of transportation ticketing by offering a more seamless, interoperable, and user-friendly experience whilst addressing the challenges associated with traditional ticketing systems.

Addressing the scalability issues, excessive communication overhead, and challenges in adapting to large-scale network node environments faced by the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm currently employed in consortium blockchains, this paper proposes a Double Layer Consensus Algorithm Based on RAFT and PBFT Consensus Algorithms (DLCA_R_P). The nodes in the blockchain are initially divided into several groups to form the lower-layer consensus network. Subsequently, the leaders of these groups constitute the upper-layer consensus network, creating a dual-layer consensus network structure. Within the lower-layer consensus network, the PBFT consensus algorithm is employed for consensus among the groups, while the primary accountants form the upper-layer RAFT consensus network. The algorithm incorporates a supervision mechanism and a reputation mechanism to enhance the security of the consensus network. Additionally, a grouping mechanism is introduced to transform the consensus network into a dynamic structure. Experimental results analysis demonstrates that compared to traditional PBFT consensus algorithms, DLCA_R_P reduces consensus latency by two orders of magnitude and improves throughput by one order of magnitude in a scenario with 100 nodes. Furthermore, it exhibits significant advantages over other improved algorithms. Thus, the DLCA_R_P consensus algorithm exhibits excellent scalability and can be widely applied in various scenarios within consortium blockchains.