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.
G. Ramsudhan, G. Hrudaya, Nandhakumar B.S, R B U R G U S · 5 authors
The concern for security and privacy have skyrocketed as IoT devices in smart homes gain popularity. Unauthorized access, data tampering, and cyberattacks are growing threats. Existing centralized security models, which rely on a single structure, are vulnerable to such threats and hence a more advanced strategy is needed. This paper aims to discuss the Blockchain-Enabled Secure IoT Architecture (BESIA) which use Ethereum smart contracts, permits decentralized authentication, and cryptographic hashing to secure the architecture. To increase device communication, MQTT is used as well as TLS v1.2 encryption to enhance data protection. The approach provides access control using a novel Hierarchical Trust Model (HTM) where the device authentication and registration are fully secured using SHA-256 hashing and asymmetric cryptography. By performing security audits via MitM intrusions, ARP spoofing, and MQTT injection attacks, the systems demonstrated a 68% increase in attack resistance and 85 % decrease in unsolicited access tries after the blockchain was integrated. To conclude, this provides a prescription for a self-sustaining and scalable framework which utilizes real-time encryption and automatic attack intervention combined with the distributed ledger technology (DLT) to enhance smart home security in IoT ecosystems.
Osama A. Khashan, Nour M. Khafajah, Nachaat Mohamed, Waleed Alomoush · 5 authors
With the rapid growth of Internet of Things (IoT) devices, securing their communication and authentication has become a critical challenge. Traditional authentication methods struggle in heterogeneous IoT systems due to incompatibility with diverse devices, reliance on centralized models, costly cryptographic operations, and issues with scalability, trust, and single points of failure. To address these challenges, this paper presents a decentralized authentication framework for IoT nodes based on blockchain technology and digital certificates. In this approach, each IoT device is equipped with a digital certificate that authenticates its identity, while the blockchain is used to securely validate these certificates in a decentralized manner. A lightweight blockchain architecture employing a Proof of Stake (PoS) consensus mechanism is proposed to validate nodes’ certificates. The consensus is conducted on these certificates, with blocks created and appended to the chain, reducing the computational and storage overhead of traditional blockchains. By leveraging blockchain’s immutability and transparency, the system enhances IoT node authentication security and integrity while minimizing resource consumption and delays. Designed for heterogeneous IoT systems, the architecture ensures seamless communication, security, scalability, and efficiency.
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.
Security and trust are two critical challenges of IoE, which refers to the interconnection of electronic devices providing intelligent services. The IoE ecosystem works using smart contracts based on pre-specified rules and dynamic automated transactions that self-eject contracts, permitting the removal of intermediaries from the process. This dynamism reduces human error and fraud while enhancing assurance and transparency. The chapter offers insights into the data integrity, better authenticity, and efficiency of identified communications provided by smart contracts in IoE architectures. Smart contracts are essentially platforms through which security and public confidence in IoE are reinforced by alleviating risks. Because they depend on tamper-free immutable blockchain ledgers, event information is recorded safely, creating immense efficiency, command over fraud, and been further improved with standards. They scale, interoperate, and comply with regulation—three key points—when constructing a secure and trusted IoE ecosystem.
Yongsheng Cao, Caiping Zhao, Yihong Zhang, Yaohui Jin
The availability of conventional mobile edge computing (MEC) for vehicles is often hindered by signal interference and attenuation, limiting its efficiency in supporting computationally intensive and latency-sensitive applications. To address these challenges, we propose a novel blockchainenabled vehicular mobile edge computing (VMEC) system that enhances resource sharing and energy efficiency in electric vehicle (EV)-centric services. The system employs an improved RAFT-based consensus mechanism (mRAFT), which dynamically evaluates the reputation of access point (AP) nodes based on their available resources, ensuring fair leader election and enhancing consensus reliability and efficiency. Furthermore, a probabilistic model is introduced to describe AP behaviors, improving the security of the consensus process. To minimize overall energy consumption, we develop a decentralized optimization framework using the Alternating Direction Method of Multipliers (ADMM). This framework jointly optimizes AP clustering, computation resource allocation, and bandwidth scheduling to achieve energy-efficient task offloading and consensus. Simulation results demonstrate that the proposed VMEC system reduces latency by 29.53 and energy consumption by 43.43 schemes, showcasing its effectiveness in delivering low-latency, energy-efficient services for advanced vehicular applications.
Hu Xia, Christian Nii Aflah Cobblah, Qi Xia, Jianbin Gao
Recently, the adoption of IoT (Internet of Things) and Blockchain has become a hot topic, particularly in areas such as education and industry. IoT involves billions of devices connected worldwide and the management of these devices is largely based on centralized systems. Thus, users will have no choice but to trust these systems. Blockchain (BC), a distributed immutable time-stamped ledger that provides decentralization, immutability, and high security can help solve some of the problems inherent in the IoT landscape. However, integrating blockchain and IoT is not trivial; it comes with some difficulties such as scalability problems, high computational costs, and overheads among others. Therefore, this paper presents a scalable and lightweight Blockchain IoT service system using multi-edge servers that reduces computational overhead by 42% and improves transaction throughput to 658 tps, representing a 2.12-3.76× improvement compared to existing approaches. Our architecture uniquely combines blockchain, group signature, and message authentication code to ensure dependable auditing of users’ access records, anonymous authentication of smart home members, and effective verification of the home management system while maintaining a memory footprint of 4.2 MB, 60-70% smaller than conventional blockchain implementations. Additionally, our solution achieves 21-46% lower communication overhead (240 bytes per transaction) and 55-73% reduced latency (850 ms), demonstrating significant improvements across all performance metrics. The distributed nature of our multi-edge server approach eliminates single points of failure and enables a transaction processing capability that scales linearly with network growth, addressing key limitations in current blockchain-IoT integrations.
Blockchain technology uses a consensus mechanism to create and finalize blocks. The consensus mechanism affects the total performance parameters of the blockchain network, such as throughput. In this paper, we present “Nazfast”, a simplified proof of stake—Byzantine fault tolerance based consensus mechanism to create and finalize blocks. The presented consensus is completed in multiple folds. For block producer and validation committee selection, we used a secure and speeded-up election mechanism, S&Sem, in Nazfast. The consensus is designed for fast block finalization in a malicious environment. The simulation result shows that we approximately achieved three block finalizations in 1 s with almost similar latency. We reduced and fixed the number of validators in the consensus to improve the throughput. We achieved a higher throughput among other consensus of the same family. Because we reduced the number of validators, the safety parameters of the consensus are at risk, so we used Sea Shield to improve the overall consensus safety. This is another blockchain to save nodes’ details when they join/unjoin the network as validators. By using all three parts together, our system is protected from 28-plus different attacks, and we maintain a high decentralization by using S&Sem. Finally, we also enhance the incentive mechanism of consensus to improve the liveness of the network.
Objective: The combination of 5G connectivity and edge computing known as 5G Edge, lifts the limitations of the Internet of Medical Things (IoMT) and enables a plethora of authentic healthcare services, including access to medical information and diagnosis. Additionally, there is a chance that malicious insiders using the 5G Edge platform could compromise the security and confidentiality of IoMT data. As a result, end users cannot trust 5G Edge data. Materials and methods: This paper imagines a new hierarchical blockchain edge of things (HBEoT) architecture that would facilitate healthcare applications managed by blockchain at the network edge. Additionally, the article delves into how HBEoT can offer security services such as authenticating users, protecting data, detecting attacks, and managing trust. First, the consensus method PoS generates a block for healthcare data, and its hash values are kept in the blockchain. The Zero Knowledge Proof (ZKP) protocol stores secret information in the blockchain, authenticating healthcare systems for enhanced privacy and security. Results: To ensure immutable data storage, the suggested architecture incorporates 5G Edge servers into a blockchain platform. It prevents unauthorised users from accessing healthcare services by acting as an anonymous authenticator. We assessing the proposed methods efficiency in terms of latency, throughput, communication cost, energy consumption cost and the validation between ZKP prover and verifier. Conclusion: We conduct multiple experiments to evaluate the effectiveness of the suggested framework. To further investigate the quality measures, such as authenticate latency, throughput rate was examined. To fulfil the requirements for a 5G-enabled healthcare system, the research shows that the suggested HBEoT-ZKP performs a decrease in latency of around 1.16 s and an improvement in throughput of approximately 339 tps. Communication and energy cost is evaluated and compared with other conventional methods.
Faced with multiple societal challenges, the healthcare sector has been compelled to leverage recent and emerging technologies to adapt. Blockchain is one of the leading technologies, offering transparency, process automation, immutability of traces and the ability to scale up in terms of both the volume of processes and the number of players interacting. The goal of the paper is to show the potential of blockchain technology - alone or merged with other technologies - to help the healthcare system evolve and provide scalable, efficient and secure solutions to four healthcare applications: electronic health record (EHR) storage, health data sharing, remote patient monitoring, and pharmaceutical supply chains. After identifying the functional and security requirements of healthcare systems, the paper conducts an in-depth review of the literature. The survey assesses the effectiveness of blockchain-based solutions in meeting functional, privacy and security needs. It is completed by an analysis of the synergies that can be expected between blockchain and emerging technologies, e.g. artificial intelligence, federated learning, the Internet of Things (IoT), and Large Language Models (LLM), to the benefit of security or privacy in healthcare.
Aryan A Ayare, Vaishnavi A Jadhav, Mustafa K Banatwala, Shashank V Changlere · 6 authors
Blockchain is a decentralized and distributed ledger technology that ensures data security, transparency, and immutability, making it a promising solution for academic record management. Currently, academic records are managed through centralized databases controlled by educational institutions, relying on manual processes, institutional servers, and third-party services. These systems are prone to inefficiencies, data breaches, and authentication challenges, often requiring time-consuming verification processes vulnerable to fraud. Blockchain technology addresses these limitations by offering a decentralized, tamper-proof framework that enhances security, accessibility, and trust in academic credential verification. This study reviews various blockchain platforms, consensus mechanisms, and scalability solutions, with a focus on Hyperledger Fabric and Ethereum, assessing their applicability in educational contexts. Furthermore, off-chain storage techniques like InterPlanetary File System, consensus algorithms, and access control mechanisms are analyzed to optimize the efficient and secure management of sensitive academic data. By integrating blockchain technology, educational institutions can modernize record-keeping, streamline verification processes, and enhance trust in academic credentials, ultimately creating a more secure and transparent academic record management system. Statistical analysis further highlights blockchain's growing adoption in education, demonstrating its effectiveness in reducing fraud, improving accessibility, and ensuring data integrity.
Chiedu Charles Chiedu, Keke Gai, Yihang Wei, Kai Ding
Blockchain technology, providing security, trust, and transparency for a variety of applications, has become a revolutionary force in decentralized systems. However, scalability continues to be a major obstacle, restricting its use in large-scale and high-throughput settings. To assess the trade-offs between decentralization, security, and computing efficiency, our survey provides a thorough examination of both on-chain and off-chain scalability options, such as sharding, adaptive block sizing, rollups, state channels, and sidechains. Layered protocols (Layer 1 and Layer 2) are also discussed in terms of scalability and interoperability. By combining on-chain, off-chain, and modular blockchain designs, this work investigates new hybrid models, multi-layer architectures, and cross-chain scalability mechanisms in addition to conventional solutions. By identifying critical gaps and challenges in existing approaches, this survey provides a roadmap for future research in scalable, secure, and interoperable blockchain infrastructures, with applications in decentralized finance, IoT, and cross-border transactions.
The Metaverse—which is a burgeoning virtual environment where users engage in social, educational, and commercial activities—has introduced new complexities in digital payment systems. This chapter examines the various vulnerabilities inherent in Metaverse payment systems, ranging from smart contract flaws to phishing and social engineering attacks. As these systems often involve real-world financial transactions or cryptocurrencies, they present attractive targets for exploitation. Through case studies and analysis, this chapter highlights common attack vectors, including quick response (QR) code manipulation, identity theft, and API vulnerabilities, while also providing mitigation strategies such as smart contract best practices, secure authentication mechanisms, and user education. This chapter also discusses future challenges, such as the evolving threat landscape posed by artificial intelligence (AI) and quantum computing, and the growing influence of decentralized finance (DeFi) within the Metaverse. The findings underscore the urgent need for robust security measures and collaborative efforts to protect users and ensure the integrity of payment systems in this rapidly evolving virtual world.
This research examines the incorporation of Artificial Intelligence (AI) in blockchain consensus algorithms, presenting an extensive overview of current improvements and anticipated effects. We conducted a thorough examination of a diverse array of academic sources, encompassing a broad spectrum of AI methodologies, such as machine learning, deep learning, and reinforcement learning, that have been applied to blockchain consensus mechanisms. The study highlights critical areas where AI can bolster blockchain performance, including enhancing effectiveness, dependability, and flexibility. Despite the promising benefits that AI integration offers, it also presents complexities and potential security risks, including data centralization and increased computational power requirements. In this analysis, we review the risks and examine the proposed mitigation strategies from existing studies, such as federated learning to preserve data privacy, secure multi-party computation to protect sensitive data, and decentralized AI marketplaces to distribute AI resources fairly. This study makes a significant contribution to the field by emphasizing the dual potential of AI to both improve and challenge blockchain systems. By advocating for balanced approaches that prioritize decentralization and security, our findings aim to provide direction for future research and practical applications in this multidisciplinary field.
Jesus Gama-Rodnguez, Ekam Puri Nieto, Juan Francisco Martínez Gil, Agustín Marín Frutos
Exploration in the crucial role of cyber threat intelligence (CTI) sharing and lifecycle security in IoT ecosystems. It examines how the ERATOSTHENES project leverages distributed ledger technology (DLT) and an inter-ledger approach to facilitate secure and privacy-preserving CTI exchange across different domains. The chapter also discusses the use of Manufacturer Usage Description (MUD) files, including the proposed Threat MUD extension, to manage security configurations and mitigation actions throughout the device lifecycle. Additionally, it highlights the integration of these components to achieve a system that dynamically responds to cybersecurity incidents, ensuring the ongoing protection of devices and domains.
Vehicular Ad Hoc Networks (VANETs) are essential to intelligent transportation systems (ITS), enabling secure, real-time communication among vehicles and infrastructure. However, their decentralized and dynamic nature makes them vulnerable to threats such as Sybil attacks, message forgery, replay attacks, and Denial-of-Service (DoS). This paper presents VANETGuard, a lightweight scalable trust management system that enhances security and scalability in 5G-enabled smart vehicular networks. The proposed system integrates entropy-based anomaly detection, Bayesian inference for adaptive trust scoring, and a lightweight distributed ledger for decentralized, tamper-resistant trust storage. Large-scale simulations under realistic traffic and attack conditions demonstrate that VANETGuard achieves 99.97% detection accuracy, significantly reduces false positives, and maintains low latency and computational overhead while supporting over 300 vehicles. These results highlight VANETGuard’s potential to enable secure, efficient, and scalable trust mechanisms in next-generation ITS and urban mobility systems.
Antonio Villafranca, Igor Tasic, Victor Gallegos, Almudena Giménez · 7 authors
Distributed Ledger Technologies (DLT), such as Bitcoin, Ethereum, and Directed Acyclic Graphs (DAG), are being positioned as a promising solution for smart agriculture by enabling secure, decentralized, and transparent traceability systems. However, these technologies face challenges related to scalability, latency, and efficiency in IoT environments. In this study, we conduct a comparative analysis of Bitcoin, Ethereum, and DAG technologies through extensive simulations, varying transaction generation rates and network latencies. A key methodological innovation of this research is the detailed codification of agricultural data transactions, encompassing parameters such as crop type, fertilization, harvesting, and transportation, enabling a structured and scalable approach to data representation. Our results reveal that Bitcoin's robustness is hindered by its high sensitivity to latency and network load, with inclusion times exceeding 700 s. Ethereum demonstrates better adaptability, with controlled inclusion times ranging from 12.91 to 35.76 s under varying conditions. DAG outperforms both, achieving significantly lower inclusion times between 4.27 and 22.25 s, highlighting its suitability for real-time applications. To the best of our knowledge, this is the first study to provide a direct comparison of these technologies in the context of agricultural traceability, showcasing the advantages and limitations of DAG-based systems for managing and scaling agricultural IoT networks.