Blockchain-Based DLTs for Metaverse Applications Security

Distributed Ledger Technologies (DLTs), including but not limited to Blockchain, have become key enablers of digital transformation across sectors. Their core attributes — immutability, traceability, decentralization, and tamper resistance — make them well-suited for secure and transparent data management. While initially linked to cryptocurrencies, DLTs are now applied in diverse domains such as identity verification, supply chain management, secure voting, and digital asset certification.In parallel, rapid advances in artificial intelligence (AI), particularly in Generative AI, are reshaping how data is generated, interpreted and applied. AI enables new forms of automation and decision-making, but it also introduces risks related to misinformation, digital fraud and security vulnerabilities. The convergence of DLT and AI offers a promising framework to address these challenges. As decentralized trust infrastructures, DLTs can enhance the transparency and integrity of AI systems by validating identities, certifying data provenance and securing sensitive assets such as contracts, medical records and intellectual property.This integration fits within the broader BIBI paradigm – Blockchain, AI, Big Data and the Internet of Things (IoT), where DLTs provide the foundation for secure, distributed coordination and auditability. Coupled with AI, they enable automated decision-making over large-scale, heterogeneous data, particularly from IoT devices, with applications in manufacturing, logistics and health care. This synergy also unlocks advanced functionalities, including decentralized identity, verifiable credentials and trusted automation, facilitating use cases such as tokenized assets and secure digital certification.Nevertheless, several technical and ethical challenges remain. The increasing computational demands of AI raise concerns about energy consumption, an issue already encountered in early DLT deployments. Energy-efficient consensus mechanisms, such as Proof-of-Stake and Byzantine Fault Tolerance, offer valuable insights for developing more sustainable AI infrastructures. Another key challenge is interoperability, as most DLT platforms operate in isolation. Enhancing interoperability would enable seamless data and asset exchange across heterogeneous ledgers, contributing to a more integrated and user-centric digital ecosystem.Looking forward, the convergence of DLT and AI holds significant potential for intelligent smart contracts, advanced traceability in critical sectors such as food safety and health care, and the development of digital twins for real-time simulation and optimization of organizational processes. This cross-disciplinary innovation can drive not only technological and economic growth but also democratic access to trusted, privacy-preserving, and sustainable digital systems.The Special Issue “Distributed Ledger Technologies and Artificial Intelligence” aims to offer a dedicated forum for researchers exploring the intersection of these two rapidly evolving fields. A total of 13 submissions were received, and following a rigorous double-blind peer review process, with at least three reviews per manuscript, three articles were selected for publication.The first article, “Energy: Reducing Latency in IoT DLTs for AI-Driven Real-Time Solutions” (Moya Perez et al., 2025), addresses the challenges of integrating IoT networks with DLT and AI, focusing on latency and energy efficiency. The authors propose Energy, a novel consensus algorithm designed for public DAG-based DLTs, which reduces or eliminates Proof-of-Work requirements. Experimental results demonstrate substantial improvements in low-payload scenarios common in IoT, enabling real-time AI model updates and efficient, secure data flow in large-scale environments.The second paper, “eFLEET: A Framework in Federated Learning for Enhanced Electric Transportation” (Ruiz de Gauna et al., 2025), presents a federated learning approach for optimizing autonomous electric vehicle routing in urban settings. Using traffic image analysis through computer vision and a DAG-based privacy layer, the framework selects optimal routes based on real-time and predictive data. The experiments show how the system improves traffic planning, enhances scalability and contributes to energy efficiency and pollution reduction in smart cities.The third contribution, “Enhancing the Viewing, Browsing and Searching of Knowledge Graphs with Virtual Properties” (Dibowski, 2024), introduces virtual properties as a user-interface enhancement for knowledge graph exploration. Defined using SHACL and evaluated via SPARQL queries, virtual properties enrich class scopes beyond direct neighbors. Successfully implemented at Bosch for over 100,000 users, this novel approach improves usability and expressiveness in knowledge-driven applications and sets a new direction for SHACL-based UI development.The authors would like to express their sincere gratitude to all the authors who submitted their contributions and to the anonymous reviewers for their thoughtful and rigorous evaluations. The authors also extend their appreciation to Professor Honghao Gao, Editor-in-Chief of IJWIS (Emerald), for his support in the publication of this special issue. The authors believe the selected papers represent cutting-edge research in this area and will be of great interest to the DLT and AI research communities.

The latest study sheds light on distributed ledger technologies (DLTs) outside blockchain systems. The first section of this article introduces DLTs, focusing on blockchain as the main paradigm. It highlights three critical characteristics of blockchain: decentralisation, transparency, and security, and emphasises how blockchain is transforming various industries, including supply chain management and finance. Subsequently, the discussion shifts to new developments and approaches in the DLT space. It introduces next-generation ledgers designed to address traditional blockchains' scalability, energy efficiency, and interoperability challenges. The study delves into modern innovations that achieve higher transaction speeds and greater flexibility, such as hybrid models and directed acyclic graphs (DAGs). A significant portion is dedicated to how these advanced DLTs are used to transform sectors like healthcare government, secure patient data management, and enhance transparency and citizen participation. The article also addresses the challenges and ethical considerations of using these technologies. Finally, the paper predicts that DLTs will improve efficiency and innovation in industries outside blockchain technology. To maximise these new technologies' potential, research and interdisciplinary collaboration are essential.

Chapter 11 - Applications of blockchain technology for improving security in the internet of things (IoT)

The Internet of Things (IoT) paradigm aims to enhance human society and living standards with the vast deployment of smart and autonomous devices, which requires seamless collaboration. The number of connected devices increases daily, introducing identity management requirements for edge IoT devices. Due to IoT devices' heterogeneity and resource-constrained configuration, traditional identity management systems are not feasible. As a result, identity management for IoT devices is still an open issue. Distributed Ledger Technology (DLT) and blockchain-based security solutions are becoming popular in different application domains. This paper presents a novel DLT-based distributed identity management architecture for edge IoT devices. The model can be adapted with any IoT solution for secure and trustworthy communication between devices. We have comprehensively reviewed popular consensus mechanisms used in DLT implementations and their connection to IoT research, specifically identity management for Edge IoT devices. Our proposed location-based identity management model is generic, distributed, and decentralized. The proposed model is verified using the Scyther formal verification tool for security performance measurement. SPIN model checker is employed for different state verification of our proposed model. The open-source simulation tool FobSim is used for fog and edge/user layer DTL deployment performance analysis. The results and discussion section represents how our proposed decentralized identity management solution should enhance user data privacy and secure and trustworthy communication in IoT.

Advancements in distributed ledger technology for Internet of Things

Digital Vehicle Protocol based on Distributed Ledger Technology in Production

IoT devices rely on authentication mechanisms to render secure message exchange. During data transmission, scalability, data integrity, and processing time have been considered challenging aspects for a system constituted by IoT devices. The application of physical unclonable functions (PUFs) ensures secure data transmission among the internet of things (IoT) devices in a simplified network with an efficient time-stamped agreement. This paper proposes a secure, lightweight, cost-efficient reinforcement machine learning framework (SLCR-MLF) to achieve decentralization and security, thus enabling scalability, data integrity, and optimized processing time in IoT devices. PUF has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device communication. An IoT network gathers information of interest from multiple cluster members selected by the proposed framework. In addition, the software-defined secured (SDS) technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT platform. Simulation analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime, energy, secured data retrieval rate, and performance ratio. By enabling the proposed framework, number of residual nodes is reduced to 16%, energy consumption is reduced by up to 50%, almost 30% improvement in data retrieval rate, and network lifetime is improved by up to 1000 msec.

Internet of Things has emerged as a key techno-logical enabler for broader socio-technical and socio-economic paradigms, such as smart cities and Circular Economy. However, IoT systems are characterised by constraints and limitations which in order to be overcome they need to be deployed in conjunction and in synergy with other emerging ICT. Distributed Ledger Technologies (DLT) can help overcome challenges pertaining to data immutability, timeliness and security. However, the use of DLT does not satisfactorily mitigate security risks and vulnerabilities per se and currently cybersecurity aspects of IoT systems are addressed in a fragmented way. Furthermore, the conflict between the resource demanding Blockchains and the highly constrained nature of IoT devices hinders implementation efforts of corresponding systems. We consider networked systems that comprise both IoT and DLT technologies via the prism of Intelligent Transportation Systems (ITS). We elicit a three-tier threat model identifying attack vectors at the Device, the Network and the DLT layers. The identified attacks are then ranked by using the DREAD ranking scheme. The use of the threat model is demonstrated on a novel proof-of-concept IoT networked system implemented using the IOTA Tangle distributed ledger, where it helps to critically appraise the design of the system against the most critical attacks. Furthermore, the developed system is among the first in the literature to demonstrate the synergy of IoT and DLT on actual constrained embedded devices. The performance evaluation provides insights showing that such systems can be efficient and suitable for real-life deployment.

Data quality is a bottleneck for efficient machine-to-machine communication without human intervention in Industrial Internet of Things (IIoT). Conventional centralised data quality management (DQM) approaches are not tamper-proof. They require trustworthy and highly skilled intermediation, and can only access and use data from limited data sources. This does not only impacts the integrity and availability of the IIoT data, but also makes the DQM process time and resource consuming. To address this problem, a blockchain based DQM platform is proposed in this paper, which aims to enable tamper-proof data transactions in a decentralised and trustless environment. To fit for different quality requirements, our platform supports customisable smart contracts for quality assurance. And to improve our platform’s performance, we discuss and analyze different distributed ledger technologies.

Advancements in distributed ledger technologies are driving the rise of blockchain-based social media platforms such as Steemit, where users interact with each other in similar ways as conventional social networks. These platforms are autonomously managed by users using decentralized consensus protocols in a cryptocurrency ecosystem. The deep integration of social networks and blockchains in these platforms provides potential for numerous cross-domain research studies that are of interest to both the research communities. However, it is challenging to process and analyze large volumes of raw Steemit data as it requires specialized skills in both software engineering and blockchain systems and involves substantial efforts in extracting and filtering various types of operations. To tackle this challenge, we collect over 38 million blocks generated in Steemit during a 45 month time period from 2016/03 to 2019/11 and extract ten key types of operations performed by the users. The results generate SteemOps, a new dataset that organizes more than 900 million operations from Steemit into three sub-datasets namely (i) social-network operation dataset (SOD), (ii) witness-election operation dataset (WOD) and (iii) value-transfer operation dataset (VOD). We describe the dataset schema and its usage in detail and outline possible future research studies using SteemOps. SteemOps is designed to facilitate future research aimed at providing deeper insights on emerging blockchain-based social media platforms.

The design of distributed ledger, Asymmetric Key Algorithm (AKA) blockchain systems, is prominent in administering security and access control in various real-time services and applications. The assimilation of blockchain systems leverages the reliable access and secure service provisioning of the services. However, the distributed ledger technology’s access control and chained decisions are defaced by pervasive and service unawareness. It results in degrading security through unattended access control for limited-service users. In this article, a service-aware access control procedure (SACP) is introduced to address the afore-mentioned issue. The proposed SACP defines attended access control for all the service session by identifying the users and service provider availability. The distributed nature of the ledger systems and classification tree learning are combined to determine unattended access. The sole access is determined by summarizing the closed and open access requests and the service provider’s availability and integrity checks. In this process, the learning process classifies the secured access request and completed the integrity checks of the current and previous service dissemination. This classification-based access administration reduces the service disconnections and false access rate of the applications.

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS.

Purpose of the study. The first goal of the study is a comparative analysis of the models of the accounting system of the stock market operating on the territory of the Russia, in particular, the model of the distributed accounting system and the model of the centralized accounting with the use of the central depository are considered. The second goal is to apply the distributed ledger technology in the development of a new model for the stock market accounting system. The goals are conditioned by the growing interest to the technology and similarity of its internal processes with the system of accounting of digital assets. The needs in optimization of the existing accounting system, bringing the system to the logical centralization are revealed. The application of distributed ledger technology for the purpose of unification of depository accounting systems is proposed. The analysis of efficiency of the given models is carried out and the criteria of efficiency estimation of each model are revealed. Materials and methods. For carrying out the research the works of description of register system genesis of stock market in Russia were applied. Works of Russian and foreign researchers in the field of influence of technology of distributed registers on accounting system and business processes of organizations are studied. The methodology for comparing models was the approach to the development of highly specialized evaluation criteria that meet the needs of the largest number of stock market participants. Results. Evaluation criteria were developed, on the basis of which the comparison of the existing models of the accounting system of stock market and the projected model on the distributed ledger technology was carried out. The needs for optimization of the existing accounting system of Russia, bring the system to the logical centralization of interaction of elements are revealed. Suggestions for the use of distributed ledger technology in order to unite the register systems of obligations have been developed The variety of elements of the accounting system of the Russian stock market was determined a large number of indicators used in comparisons of models, as well as a variety of approaches, both to the construction of the indicators themselves and to the collection of data to ensure their calculation and comparison. Conclusion. According to the results of the research it was determined that the decentralized model of the stock market accounting system on the distributed ledger technology in comparison with the classic model reduces the transaction cost of obligation right accounting operations, optimizes the complexity of the processes of interaction between the elements of the system, such as depositories and registrars, increases the transparency of the stock market, the processes of the transition of assets rights, due to which the liquidity of such assets and the increases investors interest. Decentralized model standardizes the of communication data, which has a positive effect on the indicators of information systems of participants. In comparison with the centralized model, the decentralized model eliminates the central depository transaction commission, eliminates monopolization to choose the direction of stock market depository accounting system development. Finally, the positive influence of the decentralized model on the stock market as a whole was revealed. The resulting form of relations will reduce the main cost indicators of the accounting system elements, such as transaction costs, the cost of support and development of information systems, the number of necessary formats for the application of interaction with counterparties.

This review proposes a comprehensive framework that integrates data fusion with Distributed Ledger Technologies (DLT) to enhance sustainability in supply chain management. In today’s global supply chains, ensuring transparency, efficiency, and environmental responsibility is critical, yet the lack of real-time visibility and data fragmentation presents significant challenges. The framework addresses these issues by merging data from multiple sources, including IoT devices, operational databases, and external environmental factors, using advanced data fusion algorithms. DLT, with its decentralized, immutable, and transparent nature, ensures the integrity and security of the data, allowing all stakeholders to access accurate and tamper-proof information. The fusion of data within a DLT infrastructure not only improves traceability and accountability but also enables the automation of sustainability checks via smart contracts. These contracts can trigger actions based on predefined sustainability metrics such as carbon emissions, energy consumption, and resource efficiency. Furthermore, predictive analytics and machine learning algorithms integrated into the system provide real-time monitoring and optimization of sustainability performance throughout the supply chain. The proposed review offers numerous benefits, including enhanced transparency, reduced operational costs, improved sustainability outcomes, and risk mitigation. It also addresses challenges such as scalability, data privacy, and regulatory compliance, offering solutions to overcome these hurdles. By exploring case studies of successful implementations, this review demonstrates the practical applications and future potential of combining DLT and data fusion for sustainable supply chain management, positioning it as a critical tool for organizations aiming to meet environmental and regulatory demands in an increasingly digital and eco-conscious world.

Collecting data from massive Internet of Things (IoT) devices is a challenging task, since communication circuits are power-demanding while energy supply at IoT devices is limited. To overcome this challenge, backscatter communication emerges as a promising solution as it eliminates radio frequency components in IoT devices. Unfortunately, the transmission range of backscatter communication is short. To facilitate backscatter communication, this work proposes to integrate unmanned ground vehicle (UGV) with backscatter data collection. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper studies energy minimization under a joint graph mobility and backscatter communication model. With the joint model, the mobility management and power allocation problem unfortunately involves nonlinear coupling between discrete variables brought by mobility and continuous variables brought by communication. Despite the optimization challenges, an algorithm that theoretically achieves the minimum energy consumption is derived, and it leads to automatic trade-off between spending energy on moving versus on communication in the UGV backscatter system. Simulation results show that if the noise power is small (e.g., -100 dBm), the UGV should collect the data with small movements. However, if the noise power is increased to a larger value (e.g., -60 dBm), the UGV should spend more motion energy to get closer to IoT users.