H. Mohammed Ali, William J. Buchanan, Jawad Ahmad, Mwrwan Abubakar · 6 authors
We introduce TrustShare, a novel blockchain-based framework designed to enable secure, privacy-preserving, and trust-aware cyber threat intelligence (CTI) sharing across organizational boundaries. Leveraging Hyperledger Fabric, the architecture supports fine-grained access control and immutability through smart contract-enforced trust policies. The system combines Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with temporal, spatial, and controlled revelation constraints to grant data owners precise control over shared intelligence. To ensure scalable decentralized storage, encrypted CTI is distributed via the IPFS, with blockchain-anchored references ensuring verifiability and traceability. Using STIX for structuring and TAXII for exchange, the framework complies with the GDPR requirements, embedding revocation and the right to be forgotten through certificate authorities. The experimental validation demonstrates that TrustShare achieves low-latency retrieval, efficient encryption performance, and robust scalability in containerized deployments. By unifying decentralized technologies with cryptographic enforcement and regulatory compliance, TrustShare sets a foundation for the next generation of sovereign and trustworthy threat intelligence collaboration.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
Karthika Veeramani, Suresh Jaganathan, Venkatavara Prasad D
Attendance systems that exist currently are time consuming and vulnerable to modification.The attendance system proposed here is efficient and immutable, using facial recognition for marking and blockchain technology (BT) to secure attendance data.Though facial recognition for marking attendance has overtaken other biometric methods for its convenience, the program to mark attendance is not free from modification by some third party.The novel idea of using permissioned blockchain technology as the solution helps create an immutable privately-owned ledger in a distributed manner for storing attendance data where the code to automate the process of the attendance system is also immutable.
In recent years, several research and development initiatives have focused on developing secure and trustworthy systems for the healthcare industry via pervasive and mobile healthcare (mHealth) solutions. State-of-the-art mHealth solutions primarily rely on centralized storage, such as cloud computing servers, which may escalate the maintenance costs, require ever-increasing storage infrastructure, and pose privacy and security risks to the health-critical data produced, consumed, and transmitted over ad hoc networks. To overcome these limitations, we conducted this study intending to synergize mobile computing (devices to process health-critical data) and blockchain technology (infrastructure to secure storage and retrieval of health-critical data), specifically addressing data security and privacy using a blockchain mHealth system. The research employs an incremental method by (i) developing a framework that acts as a blueprint to architect blockchain-enabled mHealth systems, (ii) implementing a suite of algorithms as a proof-of-concept to automate the framework, and (iii) experimental evaluations to validate the scalability, computation, and energy efficiency of the proposed solution. The proposed framework has been implemented as a frontend using a mobile application interface that exploits the backend via the InterPlanetary File System (IPFS) system and Ethereum blockchain for secure management of mHealth data. We use a case-study-based approach demonstrating how health units, medics, and patients can securely access and distribute health-critical data. For evaluation, we deployed a smart contract prototype on the Ethereum TESTNET network in a Windows environment to test the proposed framework. Results of the evaluation indicate (a) scalability with query response time (range: 10–41 ms), (b) computational performance (CPU utilization: 1.5% – 2.5%), and (c) energy efficiency (gas consumption: 40000 units for 1000 bytes). The proposed solution – framework, algorithms, and experimental evaluation – aims to advance state-of-the-art architecting and implementing cybersecurity mHealth solutions using blockchain technology.
Shereen Ismail, Raouf Mehannaoui, Eden Teshome Hunde, Hassan Reza
IoT devices are typically resource-constrained, with limited computational power, storage, and energy. Holochain, an emerging distributed ledger technology (DLT), offers the benefits of blockchain while overcoming its limitations, such as the reliance on consensus algorithms and a globally synchronized ledger. As a result, Holochain has garnered attention in the research community as a promising solution for distributed IoT applications. This paper reviews various DLTs in IoT distributed networks, focusing on the motivation for utilizing Holochain in these environments. We explore its key applications, challenges, and research insights. We propose the HoloSec framework, a conceptual security framework for IoT distributed networks that leverages Holochain’s agent-centric architecture, advanced cryptography, and machine learning (ML). The paper also illustrates the setup and implementation of a Holochain-based IoT network for a healthcare scenario and compares the performance of Holochain with traditional blockchain solutions. Initial experimental results show that Holochain achieves a latency of around 50 ms for data publishing and 30 ms for retrieval, with a throughput of approximately 20 transactions per second (TPS) on a single node, significantly outperforming blockchain, which shows higher latency (200 ms publish, 100 ms retrieve) and lower throughput (10 TPS). Finally, we examine key challenges associated with Holochain and outline future research directions aimed at enhancing its interoperability, scalability, security, and regulatory compliance in IoT environments.
Ravi Kumar Munaganuri, Yamarthi Narasimha Rao, Sai Chandana Bolem
This research is anchored on the burning need for irrigation optimization and crop water use efficiency improvement, which remains a challenge in smart agriculture processes. Traditional irrigation methods normally lead to inefficiency, resulting in wasted water and non-maximum crops. These traditional ways normally lack attributes of real-time adaptability and secure data management—things that are very key to modernizing agricultural practices. In this work, artificial intelligence (AI), Internet of Things (IoT), and blockchain techniques will be integrated to design a comprehensive system for monitoring and predicting soil moisture levels. In the proposed model, long short-term memory (LSTM) networks are considered for soil moisture level prediction, taking into consideration past data, weather, and crop type. LSTM networks are chosen here for their high performance in timestamp series prediction tasks with an mean average error (MAE) of 0.02 m 3 /m 3 over a 7-day forecast horizon. For real-time monitoring, IoT sensors based on long range wide area network (LoRaWAN) technology are field-deployed for conducting long-range communications while consuming very limited energy to extend the sensor battery life over 5 years and bring down the data transmission latency below 5 s. It has an inbuilt permissioned blockchain framework—Hyperledger Fabric—which offers a secure and transparent system for data management and maintaining a record of soil moisture data, irrigation events, and metadata from sensors. This ensures the immutability and integrity of sets of data. Smart contracts automate irrigation upon reaching preconfigured soil moisture thresholds, and hence zero data integrity breaches occur with a transaction throughput of 1,000 transactions per second, taken into view with smart contract execution latency of less than 2 s. Moreover, it utilizes reinforcement learning with Deep Q-Learning to derive an optimized irrigation schedule. In this regard, it enables learning optimal irrigation policies and implements them to improve efficiency in the usage of water by 25% and increases crop yield by 15% compared to the traditional methods. Clearly from field trials, results indicate evident efficiency of the integrated system: a 20% water usage reduction and a 12% increase in crop yield within one growing season. This is rather an innovative take on irrigation practices, increasing a great deal of accuracy and sustainability for such and providing a really strong solution toward better agricultural productivity and resource management.
Bhupinder Kaur, Deepak Prashar, Leo Mršić, Ahmad Almogren · 7 authors
Wireless sensor networks (WSNs) are subject to distributed denial-of-service (DDoS) attacks that impact data dependability, mobility of nodes, and energy drain. The remedy to these challenges in this work is a solution based on deep learning integrated with a blockchain-aided distance-vector hop (DV-HOP) localization algorithm for reliable and secure node localization. Incorporating a blockchain ledger makes the network more trustworthy as it verifies usual and unusual system activities, whereas the DV-HOP algorithm mitigates localization inaccuracies and enhances node placement. The system is evaluated according to different performance measures like localization error, accuracy ratio, average localization error (ALE), probability of location, false positive rate (FPR), false negative rate (FNR), energy utilization, network stability, node failure rate, node recovery rate, and malicious node detection rate. Experimental results reveal improved security, accuracy, and efficiency with 17% FPR and 15% FNR, outperforming the conventional methods. This model enhances WSN performance in different environments via precise data transmission from the source to the destination. The results confirm that integrating deep learning with blockchain and DV-HOP increases network robustness, thus making WSNs more secure against security attacks while reducing energy consumption and localization accuracy. The proposed model presents a strong solution for real-world applications in wireless network environments.
One of the modern areas of blockchain technology application is the Internet of Things (IoT). An important component of blockchain technology is the consensus layer. It includes consensus protocols that are used to establish and maintain consensus, as well as to ensure network security, accuracy, and protection of the registry from unauthorized access. Currently, there are a large number of different consensus protocols, including those for blockchain-based IoT networks. Therefore, choosing the most suitable consensus protocol for a specific distributed ledger system, in particular, for an IoT blockchain solution, is an important task. The problem of optimal blockchain consensus mechanism selection in IoT networks can be considered a multi-criteria decision-making problem. This paper presents the step-by-step development of a conceptual model of a system of optimal consensus protocol selection for blockchain-based IoT networks. Following this step-by-step approach, the final goal is to transform the conceptual framework into a practical, adaptive, and efficient decision-making system for blockchain-based IoT networks. The obtained results can be useful for developers and researchers working in the field of blockchain technology and the Internet of Things and contribute to improving the efficiency and security of IoT networks.
Herman Zahid, Adil Zulfiqar, Muhammad Adnan, Muhammad Sajid Iqbal · 7 authors
This review explores the transformative architecture of Smart Grid 3.0 by integrating cutting-edge technologies. It presents novel architectural frameworks to transform nanogrid, microgrid, and VPP topologies to their Grid 3.0 counterparts. This study systematically analyzes the application of advanced algorithms and technologies across all hierarchical subsystems—nanogrid 3.0, microgrid 3.0, VPP 3.0, and Smart Grid 3.0. These digital technologies have transformative capabilities. The digital twins can perform real-time monitoring, simulation, and predictive analysis; blockchain ensures secure, decentralized energy transactions; and the metaverse creates immersive, interactive environments for system management. This review also explores the role of AI in power grid which is to optimize energy scheduling, fault detection, and energy management. This paper adds to the literature by systematically addressing subsystems of Smart Grid 3.0, including energy generation, transmission, distribution, communication, and storage. Challenges such as interoperability, scalability, data integrity, and cybersecurity are discussed, and solutions are proposed which highlights the need of interdisciplinary approach. These include cyber-attack detection and mitigation mechanisms, advanced simulation tools, and robust policy frameworks. A thorough review of literature enabled this paper to present practical implementation strategies and real-world examples of digital technologies integrated smart grids. By integrating these technologies across hierarchical energy systems, this study establishes a foundation for future research in transforming conventional smart grid infrastructure into a resilient, efficient, and interconnected cyber-physical energy network called Smart Grid 3.0 as the peak of this evolution so far.
In supply chain finance (SCF), the long-standing issue of "difficult and expensive financing" has hindered SMEs' growth, with blockchain technology offering a novel solution.This study adopts a theoretical framework of supply chain internal and external financing to systematically analyze financing models: internal financing for upstream manufacturers, midstream distributors, and downstream e-commerce enterprises, and external bank financing via blockchain platforms.It compares decision-making differences between traditional and blockchain-enabled financing, revealing that blockchain technology reshapes the financing landscape through three core mechanisms: information sharing via distributed ledgers, credit transmission across supply chain tiers, and cost optimization through smart contracts.The study finds that blockchain reconstructs the trust system, optimizes banks' risk pricing, and alleviates financing constraints for end-tier enterprises.Additionally, platforms dominated by different entities (e.g., manufacturers, e-commerce companies, and banks) reshape supply chain pricing and profit distribution through differentiated governance rules.These findings provide theoretical support for integrating "blockchain + SCF" and guide supply chain members in optimizing financing decisions and technology adoption strategies.
Vincenzo P. Di Perna, Marco Bernardo, Francesco Fabris, Sebastião Amaro · 6 authors
Since blockchains are increasingly adopted in real-world applications, it is of paramount importance to evaluate their performance across diverse scenarios.Although the network infrastructure plays a fundamental role, its impact on performance remains largely unexplored.Some studies evaluate blockchain in cloud environments, but this approach is costly and difficult to reproduce.We propose a cost-effective and reproducible environment that supports both cluster-based setups and emulation capabilities and allows the underlying network topology to be easily modified.We evaluate five industry-grade blockchains -Algorand, Diem, Ethereum, Quorum, and Solana -across five network topologies -fat-tree, full mesh, hypercube, scale-free, and torus -and different realistic workloads -smart contract requests and transfer transactions.Our benchmark framework, Lilith, shows that full mesh, hypercube, and torus topologies improve blockchain performance under heavy workloads.Algorand and Diem perform consistently across the considered topologies, while Ethereum remains robust but slower.
The counterfeit medication infiltration within global supply chains poses a major public health threat. To address this, a collaborative effort among governments, regulators, and pharmaceutical companies is essential to secure the global/local supply chain. This paper proposes a novel approach that leverages blockchain technology, polymorphic encryption, and cloud storage to tackle security risks and privacy concerns in medication supply chains. The framework integrates a drug supply chain decentralized application (also called SCMapp) within the Ethereum blockchain, enabling functionalities like secure supplier onboarding, encrypted data management, cloud storage integration, and efficient data retrieval. This approach aims to revolutionize drug supply chain management by enhancing security, transparency, and overall efficiency, ensuring adherence to global health regulations. A safe and effective method for managing drug supply chains is provided by the suggested Drug Supply Chain Management System. The proposed model outperformed existing solutions in terms of security, efficiency, and traceability. The combination of encryption, blockchain, and cloud storage provided a comprehensive approach to address the challenges of drug supply chain management. The comparison analysis highlighted the unique advantages of the proposed model over other methods.
In the contemporary digital age, education is no longer limited to traditional educational environments. Many educational institutions shifted to depend on the smart learning process but expressed concern about this solution due to its various challenges in securing the learning process and learners' data. By virtue of the most recent technologies like blockchain and artificial intelligence, which played a significant role in solving many challenges that faced the educational sector and overcoming issues like fake certificates, manipulation, tracking learners' activities, and predicting learners' academic performance. The study proposed a smart framework based on blockchain and deep learning to enhance smart learning processes and provide solutions for challenges in the field. The framework is intended to store the learner's data on the blockchain through the interplanetary file system and reap the benefits of securing the learner's data and ensuring its integrity, as well as ensuring the confidentiality and authentication of the users through the wallets that are created on the Ethereum private blockchain platform. Then apply the deep learning model to this secured data to predict the learner's performance. The smart contract functions also play a role in enabling the university to issue learners' certificates that are stored on the blockchain to be available and verifiable by all the nodes in the network. Based on the experimental results, deep neural networks were used to model the encrypted data that was stored on the blockchain and predict the learner's performance and achieved a high degree of accuracy (91.29%) and low loss (about 0.18) in comparison to other studies that depended on the centralized nature of the data. As well, the university blockchain's functionality was tested, and it successfully returned all the functional requirements and showed its legitimacy.
Blockchain technology has emerged as a transformative paradigm for secure, decentralized, and transparent data management. However, the rapid growth of decentralized applications (dApps), global transaction demands, and multi-chain ecosystems has exposed scalability bottlenecks in existing consensus mechanisms. Traditional models such as Proof of Work (PoW) and Proof of Stake (PoS), while effective in maintaining security, struggle with throughput, latency, and energy efficiency. Recent research highlights the potential of artificial intelligence (AI) to augment blockchain consensus by improving leader selection, optimizing validator participation, dynamically adjusting difficulty, and predicting network anomalies. This manuscript explores AI-assisted consensus mechanisms as a scalable alternative for next-generation blockchain systems. The paper conducts a comprehensive literature review of blockchain scalability challenges, outlines a methodology for integrating reinforcement learning (RL), deep learning, and predictive analytics into consensus protocols, and presents simulation-based results. Findings suggest that AI-enhanced consensus can achieve up to 70% improved throughput, reduce energy costs by 50%, and enhance fault tolerance by predicting malicious node behavior in advance. The study concludes that AI-assisted consensus mechanisms provide a sustainable path toward highly scalable, adaptive, and secure blockchain networks, with implications for finance, supply chains, IoT, and government applications.
Bassam W. Aboshosha, M.A. Zayed, Hany S. Khalifa, Rabie Α. Ramadan
Abstract Background The rapid expansion of Internet of Things applications in healthcare has created new opportunities for improving patient care through real-time monitoring and data sharing. However, this growth also introduces significant challenges related to data security, privacy, and system efficiency, especially for devices with limited processing power and energy resources. To address these issues, this study introduces a blockchain-based lightweight hashing system specifically designed for healthcare environments with resource-constrained devices. The goal is to ensure secure, efficient, and scalable handling of sensitive medical data without overwhelming the capabilities of connected devices. Results The proposed system combines a collision-resistant, lightweight hash function with blockchain technology to enhance data integrity, authentication, and privacy. The hash function minimizes computational demands, making it ideal for wearable and embedded healthcare devices. Blockchain integration enables decentralized data management, preventing unauthorized access and tampering. The system generates unique, immutable patient identifiers and protects electronic health information from common security threats, including collision attacks, Sybil attacks, and cryptographic analysis. Simulation results show improved computational efficiency, lower latency, and effective handling of high transaction volumes with minimal resource usage. Conclusions This research presents a secure and efficient framework for managing medical data in healthcare Internet of Things applications. By leveraging lightweight cryptographic techniques and decentralized data structures, the system addresses key limitations in current solutions while supporting scalability and real-world deployment. Potential applications include secure patient monitoring, real-time sharing of health data, and decentralized management of medical records. The proposed approach provides a foundation for future advancements in digital healthcare systems, particularly in remote care, emergency response, and wearable health technologies.
The use of Electric Vehicles (EV) will promote urban sustainability, decrease air pollution, and reduce noise pollution. In this landscape a new mobility concept termed shared electric mobility-as-a-service (eMaaS) has emerged over the years. Shared eMaaS comprises the seamless integration of various forms of electric transport services available via one single digital platform. Although, the current shared eMaaS solutions are based mostly on fragmented and siloed systems which has resulted to issues related to the exchange of data and services from different eMaaS providers. Therefore, there is need for integrators and enablers to achieve an inter-operable and intra-operable seamless shared eMaaS. To this end, Distributed Ledger Technologies (DLT) is proposed in this study to enable new business models for shared electric mobility solutions. As compared to conventional approaches DLT offers a transparent, cost-efficient, and decentralized services both for managing the supply and demand sides of shared eMaaS to improve public transportation. Accordingly, this article presents a DLT based business models grounded on the literature to decentralize shared eMaaS. Qualitative data is collected from Scopus and Web of Science database, and descriptive analysis is employed to analyze the collected data. Findings from this study presents use case scenarios of how IOTA tangle as a DLT using smart contracts and IOTA wallet/tokens are deployed to design novel business models for managing seamless travel experience for electric car sharing and leasing to improve public transportation.
Pierre Sedi Nzakuna, Vincenzo Paciello, A. Lay-Ekuakille, Angelo Kuti Lusala · 6 authors
The Internet of Things (IoT) demands scalable, secure, and feeless distributed ledger technologies (DLTs) to enable seamless machine-to-machine transactions. The IOTA DLT was developed to fulfill this vision through its feeless Directed Acyclic Graph (DAG) named the Tangle, whose announced upgrade to IOTA 2.0 promised feeless microtransactions and coordinator-free (Coordicide) decentralization via a Nakamoto Consensus mechanism and a Mana anti-spam system. However, its delayed decentralization and scalability limitations hindered ecosystem growth and practical IoT adoption, leading to a new ledger architecture named IOTA Rebased. This paper critically analyzes this architectural pivot and its implications for IoT applications, contrasting the abandoned IOTA 2.0 protocol-a leaderless, feeless DAG designed for the IoT-with the adoption of a Move Virtual Machine-based, object-oriented ledger secured by a Delegated Proof-of-Stake consensus via the Mysticeti protocol in IOTA Rebased. We evaluate IOTA Rebased trade-offs: enhanced programmability and speed versus compromised IoT suitability due to fees, and explore mitigation strategies such as sponsored transactions, lightweight clients, and hierarchical tiered transaction architecture to align IOTA Rebased with IoT environments where microtransactions are prevalent. A use case analysis is provided for the integration of IOTA Rebased in IoT scenarios. This study underscores the tension between technological innovation and decentralization, offering insights for balancing scalability with the unique demands of the IoT.
To meet latency constraints, fog computing takes computational assets to the network edge. Blockchain and reinforcement learning are increasingly being integrated into the Industrial Internet of Things (IIoT) to enhance security and efficiency. This study introduces a Reinforcement Learning-based Resource Scheduling Approach for Blockchain Networks in IIoT. Unlike previous studies, which mainly focus on either blockchain security or resource allocation, our approach integrates reinforcement learning for dynamic resource scheduling, improving efficiency while minimizing latency. The methodology is illustrated through a flowchart. Simulation results validate the effectiveness in multiple scenarios. Future work includes enhancing inter-node communication reliability.
Chitrita Devi, R. R. Shantha Spandana, G.V.T. Swapna, G Viswanath
This project provides a Cloud-Assisted Decentralized privacy-preserving Framework (CA-DPPF) that amalgamates cloud computing, blockchain generation, and IPFS to tackle the complexities of securely and efficaciously storing sensitive healthcare data. The framework utilizes ECDSA digital signatures and RSA encryption to assure strong person authentication and statistics safety, in accordance with present day developments in safeguarding healthcare information. IPFS is applied for scalable storage solutions, addressing the limitations of traditional centralized cloud services, as indicated in previous research. Blockchain era augments the system through supplying immutable document-preserving, mitigating the weaknesses of centralized systems. A rankings module is incorporated to guarantee the legitimacy of healthcare feedback, allowing people to assess doctors, with these checks securely documented on the blockchain to prevent manipulation. smart contracts, created in Solidity, enable secure transactions and govern user data at the Ethereum blockchain, making certain transparency and integrity in all interactions. The studies gives a spread that integrates the CHACHA20 encryption algorithm, strengthening computational efficiency and safety while complementing present encryption methods and improving usual system overall performance.
The developing Sixth-Generation (6G) network aims to establish seamless global connectivity for billions of humans, machines, and devices. However, the rich digital service and explosive heterogeneous connection between various entities in 6G networks can not only induce increasing complications of digital identity management but also raise material concerns about the security and privacy of user identity. In this paper, we design a user-centric identity management that returns the sole control to the user self and achieves identity sovereignty towards 6G networks. Specifically, we propose a blockchain-based Identity Management (IDM) architecture for 6G networks, which provides a practical method to secure digital identity management. Subsequently, we develop a fully privacy-preserving identity attribute management scheme by using zero-knowledge proof to protect the privacy-sensitive identity attribute. In particular, the scheme achieves an identity attribute hiding and verification protocol to support users in obtaining and applying their identity attributes without revealing concrete data. Finally, we analyze the security of the proposed architecture and implement a prototype system to evaluate its performance. The result shows that our proposed architecture can ensure that users effectively manage their digital identity in 6G networks.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
Queueing theory employs mathematical analysis to establish effectiveness metrics. Optimization models are then formulated using significant and efficient measures, such as data, to ascertain system efficiency and requirements. Each queuing system represents a discrete event system problem, and simulating these systems aids in addressing challenges and conducting practical performance analysis. Blockchain offers various benefits, including redistribution, accessibility, durability, reliability, constancy, anonymity, auditability, and data security. Its applications span across cryptocurrencies, financial services, reputation management, the 'Internet of Things', the sharing economy, and social and community services. Notably, foundational theory is increasingly pertinent in the blockchain field. For instance, performance analysis and optimization of blockchain systems rely on mathematical models like Markov processes and queueing theory. In smart healthcare, blockchain technology enhances disease diagnosis, patient care, and overall quality of life. Due to the substantial patient data stored on blockchain in smart healthcare architectures, queueing models are indispensable for efficient data processing. This paper leverages Markov chains to establish queueing theory for blockchain systems and assess the performance of smart healthcare architecture. A "Markovian-batch-service" queueing framework is devised for this purpose, modeling input and processing parameters essential for reliable queuing network simulations.
Sathya D, S Veena, Sangamesh Ramesh Yankanchi, Soujanya Manasa
The Internet of Medical Things (IoMT), also known as healthcare IoT, consists of interconnected medical devices and applications that enable remote monitoring of patients with chronic conditions. In existing healthcare systems, data from IoMT devices is stored in the cloud for analysis. However, major challenges include ensuring data privacy and prioritising critical health information. Rapid processing and transmission of emergency health data to hospitals are crucial for timely care, while strict privacy measures are necessary to prevent risks like data breaches, fraud, and unauthorised access to medical services. To overcome these challenges, the proposed system implements Ethereum blockchain technology and an edge AI classification algorithm on data collected in real-time. Edge computing enables instant analysis, classification, and prioritisation of health data, minimising latency and facilitating quick decision-making. Simultaneously, blockchain technology ensures robust data privacy through a secure access control mechanism. Patient information is securely stored on the blockchain and accessed via an Aadhaar card number and unique tokens. These tokens enable role-based access control, allowing authorised individuals— like doctors, nurses, patients, and relatives—to view, update, or delete specific records as needed.
The rise of Distributed Ledger Technology (DLT) is revolutionizing financial systems, introducing innovations such as programmable payments, and allowing Machine-to-Machine (M2M) payments, which are essential for Industry 4.0. Despite their potential, DLT-based financial systems face barriers, including operational efficiency, regulatory uncertainty, limited institutional acceptance, and challenges in integrating with conventional financial systems. Trigger solutions emerge as a promising approach to bridge these gaps by combining the programmability and immutability of DLT systems with the regulatory certainty and established trust of conventional financial systems. This work explores key requirements for trigger solutions to support interoperability between DLT-based and conventional financial systems, enabling high-frequency programmable payments and regulatory compliance for industry 4.0. We present a state channel–based trigger solution ( SCTS ) tailored to meet industry’s requirements, offering a blueprint for integrating advanced payment capabilities into conventional financial systems. SCTS leverages the concept of justified trust-building on technological advantages to enable scalable programmable payments. We find that SCTS enables businesses to adapt to the technological demands of Industry 4.0.
ABSTRACT The emergence of the Metaverse has introduced significant challenges in task offloading and data processing due to its virtual universe nature with immersive environments and a multitude of interconnected users and devices. The abundance of data in the Metaverse poses security challenges in local processing, necessitating traditional methods such as data transfer to Mobile Edge Computing (MEC) and subsequently to the cloud, thereby emphasizing security concerns. In this paper, a novel approach to address these challenges has been introduced: An Ethereum Blockchain‐based MEC framework uses smart contracts designed to ensure secure task offloading. It enables authentication in the Metaverse through smart contracts, followed by modeling the task offloading issue as a Markov Decision Process (MDP). To solve this MDP problem, a hybrid algorithm integrating Deep Q‐Networks (DQN) with Bidirectional Long Short‐Term Memory (Bi‐LSTM), known as BRL‐Net (Bi‐LSTM Reinforcement Learning Network), has been proposed. This framework enables secure and efficient task offloading in dynamic Metaverse environments. BRL‐Net outperforms Proximal Policy Optimization (PPO), achieving a 9.93% higher reward and greater stability. The BRL‐Net's performance across Blockchain consensus mechanisms shows Delegated Proof of Stake (DPoS) as the most efficient, reducing latency by 49.96%, increasing throughput by 10.48%, and lowering energy consumption by 50.24%, compared to Proof of Stake (PoS), thereby optimizing Metaverse performance.