Hancheng Gao, Mohammad S. Obaidat, Haiping Huang, Yizheng Xing · 6 authors
The adoption of blockchain technology in healthcare has significantly enhanced data integrity, transparency, and user privacy. However, high storage overhead and resource-intensive operations remain major challenges to its widespread deployment, particularly in large-scale or resource-constrained healthcare environments. To address these challenges, we propose STORChain, a storage-optimized blockchain framework designed for data services in healthcare. The framework introduces the Clustered Merkle Patricia Tree (C-MPT), a novel logical structure that aggregates similar transaction types to maximize storage efficiency while ensuring Proof of Inclusion (PoI). A Selective Transaction Pruning Strategy (STPS) is employed to prioritize and prune essential historical data, improving data access efficiency. Additionally, an incentive-based Delegated Proof-of-Stake (DPoS) consensus algorithm is utilized, integrating a probabilistic election mechanism to promote fairness and node inclusivity. Comprehensive theoretical analysis and practical experiment results indicate that STORChain significantly reduces storage overhead, optimizes data access, and outperforms existing schemes.
The COVID-19 pandemic has accelerated the adoption of digital health solutions such as telemedicine, Internet of Medical Things (IoMT), and AI-based diagnostics, enabling remote monitoring and contactless consultations. While IoMT devices—including wearable sensors and implantables—have enhanced continuous healthcare delivery, they have also introduced challenges related to security, privacy, interoperability, and latency. Traditional blockchain frameworks, though effective in ensuring decentralized trust and immutability, are resource-intensive and unsuitable for constrained IoMT environments. To address these limitations, this study proposes a Lightweight Blockchain–IoMT framework tailored for secure remote healthcare in the post-pandemic era. The proposed architecture follows a three-tier design: (i) the IoMT Device Layer for real-time physiological data collection, (ii) the Fog/Edge Layer functioning as blockchain gateways for authentication and pre-processing, and (iii) the Cloud Layer for storage, analytics, and decision support. By incorporating lightweight consensus mechanisms such as Proof-of-Authentication (PoAh) or Delegated Proof-of-Stake (DPoS), the system minimizes latency and energy consumption compared to Proof-of-Work. Security is reinforced through elliptic curve cryptography (ECC) and smart contracts, ensuring data confidentiality, integrity, and controlled access, while complying with global standards such as HIPAA and GDPR. Experimental analysis demonstrates that the lightweight blockchain–IoMT framework outperforms conventional blockchain models in transaction throughput, scalability, and energy efficiency. Moreover, the integration of machine learning within the cloud layer supports predictive analytics and personalized care.
Rui Han, Bin Yuan, Weizhong Qiang, Deqing Zou · 5 authors
The widespread use of IoT devices in the accommodation and hospitality sectors has created demand for temporary device-permission sharing and transfer. Prior work has largely focused on security issues in device permission sharing, with far less attention devoted to device permission transfer. However, inappropriate access control management during device permission transfer can also lead to violations of the users' expectations of control over their devices. For example, a malicious host retaining or regaining access to a camera after its permission has been transferred to a tenant. In this paper, we present the first systematic study on understanding and enhancing the security of device permission transfer in IoT leasing. To this end, we propose Forseti, a new authorization framework that leverages zero-knowledge proof and a decentralized ledger to ensure that the rights of both hosts and tenants are not violated. Our evaluation demonstrates that Forseti is effective, efficient, scalable, and compatible with existing IoT platforms.
Mario Norberto Sevilio de Oliveira Junior, http://lattes.cnpq.br/0962672305508249
O presente trabalho realiza uma análise comparativa de desempenho de contratos inteligentes implementados em duas arquiteturas distintas de blockchain: a Ethereum Virtual Machine (via Binance Smart Chain) e a Bitcoin Virtual Machine (via Bitcoin Satoshi Vision). O estudo teve como objetivo avaliar o impacto das diferenças arquiteturais no custo de transações, no tempo de execução e na ocupação de rede, a fim de identificar vantagens e limitações práticas de cada plataforma. Para alcançar esse propósito, foram desenvolvidos dois contratos inteligentes de referência: um contrato de mensagem, representativo de operações simples de armazenamento, e um contrato de contador, concebido para simular cenários de maior complexidade lógica e de estresse computacional. Ambos foram implementados em linguagens específicas das plataformas (Solidity para BSC e sCrypt para BSV) e executados em ambientes de teste controlados. A metodologia incluiu a configuração de infraestrutura experimental, a realização de testes de desempenho, a medição de custos, bem como a execução de funções de ataque para avaliar a escalabilidade sob alta carga.
The Internet of Things (IoT) embeds various modules into physical objects to connect them to the internet. However, due to the low power and limited computing capabilities of IoT devices, it is difficult to ensure data integrity and secure access control. To address these limitations, various studies have attempted to integrate blockchain technology into IoT systems. Nevertheless, challenges such as low scalability and difficulties in storing large-scale data remain. In this paper, we propose the I2D (IOTA-IPFS-DID)-IoT system to overcome these issues. The I2DIoT system integrates Internet of Things Application (IOTA), InterPlanetary File System (IPFS), Decentralized Identity (DID), and Verifiable Credential (VC). In the proposed system, collected data is uploaded to IPFS, a distributed file system, generating a Content Identity (CID). Only the CID is recorded on IOTA, a distributed ledger technology, to ensure data integrity. In addition, a DID-based authentication mechanism enables access control without a central server. Experimental results show that the I2D-IoT system successfully performs both data integrity and access control. CPU and memory usage were measured, and CPU utilization remained under 10%, except during synchronization with the IOTA network.
Akash Shinde, K. S. Radha, Eesh Pratap Singh, Amresh Kushwaha
Event ticketing systems have long faced challenges such as counterfeiting, scalping, and lack of transparency in resale markets. To address these issues, this research presents the design and development of a blockchain-based ticketing platform that leverages Non-Fungible Tokens (NFTs) to ensure secure, transparent, and verifiable ticket distribution. The primary aim of the study is to explore how blockchain technology can enhance trust, eliminate fraud, and provide users with full ownership of their tickets. The proposed system employs smart contracts to automate ticket creation, distribution, and resale, thereby minimizing the need for intermediaries. Each ticket is represented as a unique NFT, guaranteeing authenticity and enabling traceability throughout its lifecycle. The methodology involves implementing a decentralized application where event organizers can mint NFT tickets, and users can securely purchase, transfer, or resell them using blockchain infrastructure. The results demonstrate that NFT-based tickets effectively prevent duplication and unauthorized sales while providing an immutable record of ownership. Additionally, organizers gain better control over pricing policies, while buyers benefit from secure transfers and enhanced transparency. In conclusion, this platform contributes to solving long-standing issues in the ticketing industry by combining blockchain’s immutability with NFTs’ uniqueness. The study highlights the potential of decentralized technologies to revolutionize digital ticketing, improve user trust, and create a more efficient event management ecosystem.
This paper presents the design and implementation of a blockchain-secured system for monitoring driver sobriety and real-time geolocation. The proposed platform integrates a Modular Sensor Battery (MSB) for detecting alcohol concentration in exhaled air, a centralized Data Collection Platform (DC Platform) for real-time data visualization and storage, and a complementary physiological monitoring device—the IoT Fit-Bit Smart Band (IFSB)—which captures heart rate and blood oxygen saturation as alternative indicators when breath-based sensing may be compromised. The MSB, the DC Platform, integration with the IoT FitBit Smart Band, and the blockchain-based data management architecture represent the authors’ direct contribution to both the conceptual design and technical implementation. These elements are introduced as part of a unified, fully integrated system designed to enable non-invasive sobriety monitoring and secure data integrity in vehicular contexts. To ensure data authenticity, a custom Ethereum smart contract stores cryptographic hashes of sensor readings, enabling decentralized, tamper-evident verification without exposing sensitive medical information. The system was validated in a controlled experimental environment, confirming its operational robustness and demonstrating its potential to improve road safety through secure, real-time sobriety detection and geolocation tracking.
Selvaragavan S, Karuppasamy L, Yuvan Sankar NKR, R Sylaja Vallee Narayan S. · 6 authors
The Internet of Things (IoT) has significantly transformed critical domain such has healthcare, transportation, industry, and smart cities by connecting billions of devices that can gather and share data. However, as it grows rapidly, IoT systems are grappling with significant issues around data security, user privacy, and trust. Centralized systems are particularly at risk due to single points of failure, latency issues, and potential privacy breaches, which makes them less ideal for large-scale use. While blockchain technology provides a way to decentralize and resist tampering, traditional frameworks can be too resource-heavy and impractical for low-power IoT devices.This paper introduces a Lightweight Blockchain Framework designed for Secure and Privacy-Preserving IoT Systems. It combines energy-efficient consensus mechanisms, trust-based reputation management, and a hybrid approach to on-chain and off-chain storage. By utilizing edge-assisted processing, we can cut down on computational demands, and cryptographic techniques like elliptic curve cryptography and zero-knowledge proofs help maintain privacy during authentication. The framework is built with scalability, low latency, and effective communication in mind, making it suitable for real-time IoT applications.Simulation results show that our proposed framework can reduce communication costs by 35%, lower latency, and improve privacy protection compared to traditional blockchain-based IoT systems. These findings indicate that the framework is not only lightweight and secure but also practical for applications that need privacy and quick responses, such as in healthcare, industrial automation, and smart infrastructure.
Mohamed Abdessamed Rezazi, Mouhamed Amine Bouchiha, A. Bendada, Yacine Ghamri-Doudane
Roaming settlement in 5G and beyond networks demands secure, efficient, and trustworthy mechanisms for billing reconciliation between mobile operators. While blockchain promises decentralization and auditability, existing solutions suffer from critical limitations-namely, data privacy risks, assumptions of mutual trust, and scalability bottlenecks. To address these challenges, we present B5GRoam, a novel on-chain and zero-trust framework for secure, privacy-preserving, and scalable roaming settlements. B5GRoam introduces a cryptographically verifiable call detail record (CDR) submission protocol, enabling smart contracts to authenticate usage claims without exposing sensitive data. To preserve privacy, we integrate non-interactive zero-knowledge proofs (zkSNARKs) that allow on-chain verification of roaming activity without revealing user or network details. To meet the high-throughput demands of 5G environments, B5GRoam leverages Layer 2 zk-Rollups, significantly reducing gas costs while maintaining the security guarantees of Layer 1. Experimental results demonstrate a throughput of over 7,200 tx/s with strong privacy and substantial cost savings. By eliminating intermediaries and enhancing verifiability, B5GRoam offers a practical and secure foundation for decentralized roaming in future mobile networks.
Bad medical debt negatively impacts individuals' physical and mental well-being, can discourage future care-seeking, and is increasingly viewed as a social determinant of health. Addressing this issue by linking the healthcare sector with humanitarian efforts presents a significant global challenge, requiring innovative solutions. This paper aims to synthesize existing literature on the topic. Using the Scopus database, a systematic review of literature was conducted from 1990 to 2024, employing the PRISMA framework. Thematic analysis was applied to organize and interpret the findings. Reviewing 958 papers, including 318 key sources, revealed a pressing need for a SMART blockchain healthcare platform. Such a platform would securely connect beneficiaries with philanthropists, facilitate data sharing, and align donor requirements with beneficiary criteria. Employing decentralized autonomous organization frameworks and smart contracts ensures transparency, efficiency, and accountability through automated and secure processes.
This research presents a unified and intelligent security framework for cloud computing environments by integrating Quantum Neural Networks (QNNs), Zero-Knowledge Proof Engines (ZKEs), and Blockchain technology. As the scale and complexity of cloud infrastructures grow, traditional security mechanisms are proving insufficient against advanced cyber threats such as Distributed Denial of Service (DDoS), malware attacks, and Man-in-the-Middle (MITM) exploits. The proposed framework leverages the computational power of quantum systems to enhance the performance of Deep Learning models, enabling faster and more accurate threat detection. ZKEs provide privacy-preserving authentication by verifying user identities without revealing sensitive information, while Block chain ensures data integrity, decentralization, and tamper-proof transaction records. Experimental evaluationconducted using IBM Qiskit and a honey net-integrated Deep Neural Network (DNN) demonstrates a significant improvement in detection accuracy, reduced response time, and enhanced system resilience compared to conventional approaches. The results validate the effectiveness of the multi-layered model in addressing real-world cloud security challenges. This research contributes a scalable, privacy-centric, and quantum-secure architectural foundation for the next generation of cloud-based systems
Blockchain sharding is a promising approach to improving system scalability. However, traditional designs rely on lock-based cross-shard commit protocols, which introduce significant performance bottlenecks due to repeated on-chain communication and consensus. The emergence of complex cross-shard contracts further exacerbates these issues. Although recent off-chain execution models reduce on-chain overhead by decoupling contract execution from consensus, they still incur high communication costs and struggle to maintain state consistency. To address these challenges, this paper presents a sharding framework that seamlessly integrates on-chain and off-chain processing. By leveraging Trusted Execution Environments (TEEs), the framework enables secure and efficient off-chain execution of cross-shard smart contracts. It incorporates an off-chain execution hub for verifiable contract execution and a state-aware cross-shard commit protocol to guarantee correctness. Furthermore, a genetic algorithm-based contract-migration strategy dynamically reduces cross-shard interactions. Prototype evaluations show that the proposed framework significantly outperforms mainstream sharding solutions, achieving at least 2.1× higher throughput and reducing cross-shard transaction latency by over 52.6%.
The speedy development of the Internet of Things (IoT) needs safe and efficient and scalable data transmission systems that can resist privacy violation and inefficiency within the network. The paper introduces a blockchain-based IoT transmission framework that inculcates Paillier Homomorphic Encryption (PHE) to end-to-end data transmission security as well as Practical Byzantine Fault Tolerance (PBFT) consensus mechanism as a low-latency trust establishment methodology. The model has been written in Python and tested with the Intel Lab IoT Sensor Dataset. The proposed system has an average success rate in secure data encryption of up to 99.97 percent and acceptable percentages of bit error with tolerance limits on all packet lengths (1 KB and above) as depicted in experimental results, thus with low computation needs (3.2 ms encryption, 3.4 ms decryption) and moderate memory consumption (120 KB). The network performance analysis proves that smaller block sizes (5 KB) provide 245 Tx/s throughput using 0.2 J energy, thus, being utilized in real-time IoT operation. PBFT protocol achieves high levels of latency (90 ms) and finality (1.1 s) reduction than the Proof-of-Work and Proof-of-Stake, and uses up to 96 percent less energy than PoW. Using homomorphic encryption with lightweight consensus is a good compromise between security, scalability, and achievable energy efficiency that fits well in next-generation IoT deployments where trust, privacy, and performance are of utmost importance.
Md Bokhtiar Al Zami, Md Raihan Uddin, Dinh C. Nguyen
Federated learning (FL) has gained popularity as a privacy-preserving method of training machine learning models on decentralized networks. However to ensure reliable operation of UAV-assisted FL systems, issues like as excessive energy consumption, communication inefficiencies, and security vulnerabilities must be solved. This paper proposes an innovative framework that integrates Digital Twin (DT) technology and Zero-Knowledge Federated Learning (zkFed) to tackle these challenges. UAVs act as mobile base stations, allowing scattered devices to train FL models locally and upload model updates for aggregation. By incorporating DT technology, our approach enables real-time system monitoring and predictive maintenance, improving UAV network efficiency. Additionally, Zero-Knowledge Proofs (ZKPs) strengthen security by allowing model verification without exposing sensitive data. To optimize energy efficiency and resource management, we introduce a dynamic allocation strategy that adjusts UAV flight paths, transmission power, and processing rates based on network conditions. Using block coordinate descent and convex optimization techniques, our method significantly reduces system energy consumption by up to 29.6% compared to conventional FL approaches. Simulation results demonstrate improved learning performance, security, and scalability, positioning this framework as a promising solution for next-generation UAV-based intelligent networks.
Gabriel Babatunde Iwasokun, Oluwaseyi Segun, Samuel Oluwatayo Ogunlana, Michael Adegoke · 6 authors
The integration of Internet of Things (IoT) devices into modern payment systems has introduced innovative functionalities, but also significant security and performance challenges. IoT devices, such as smart sensors, wearables, and automated vending machines, are typically resource-constrained yet handle sensitive financial transactions that demand robust security mechanisms. Conventional cryptographic solutions are often unsuitable for these environments due to their high computational and memory requirements. This paper presents the design of a lightweight blockchain-based model to secure IoT payment systems by leveraging the Ethereum blockchain and AES-128 encryption. The blockchain token is encrypted with AES-128 to add layer of security before being stored in a database. The model is designed to employ a decentralised digital ledger to record and validate transactions without a central authority, and the transaction is grouped into a block and linked to the preceding block through cryptographic hashes. The chain of blocks forms an immutable record that enhances transparency and security, and the distributed nature of blockchain networks, wherein multiple participants validate each transaction, minimises the risk of fraudulent activities while ensuring consensus is achieved through predefined protocols. Analysis of results from the implementation established the minimization of computational overhead and robust security measures, and was particularly beneficial where the scalability of decentralized systems is required alongside heightened security protocols.
Internet of Underwater Things (IoUT) introduces critical security challenges, especially for protecting distributed infrastructures in resource-constrained environments. Conventional asymmetric and centralized authentication models are unsuitable due to computational and communication overhead, while symmetric approaches lack robustness without trusted storage or hardware. We propose a non-interactive, asynchronous authentication protocol based on NIZKP, combining PUFs-derived secrets with decentralized identifiers on a distributed ledger. This approach enables direct node authentication with cryptographically verifiable identity binding, minimal resource usage, offline verification, and full support for asynchronous operation in constrained environments. The protocol is formally analysed and implemented on COTS hardware without additional secure components. Evaluation shows low energy consumption (827.2 mJ), minimal communication overhead (113 B, 1.513s, 817.9 mJ), and reasonable execution times (worst case ≈ 5.310s), outperforming state-of-the-art solutions in the first four metrics.
Haruki Kurisaka, Yue Su, Phi Le Nguyen, Kien Nguyen · 5 authors
Abstract The integration of IoT with blockchain technology enhances security and privacy through decentralized, trust-based systems, addressing challenges like single points of failure and limited scalability in traditional IoT architectures. This study evaluates the performance of Ethereum-based IoT systems using resource-constrained devices (Raspberry Pi 4 and Raspberry Pi 3) on a private blockchain. Performance metrics, including CPU, memory, disk usage, power consumption, and latency, were analyzed across three consensus mechanisms: Proof-of-Work (PoW), Proof-of-Authority (PoA), and Proof-of-Stake (PoS). To address the blockchain’s latency performance, we introduced the metrics Transaction-oriented latency (ToL) and Block-oriented latency (BoL) to characterize latency under PoS, capturing the distinctive dynamics of PoS. Our findings show that PoA achieves the lowest resource consumption, with CPU usage reduced by 98% compared to PoW and 20% compared to PoS, and power consumption decreased by 50% from PoW and 14% from PoS. Further, to assess blockchain scalability, we varied transaction transmission rates under PoA, identifying its impact on performance. These findings provide practical guidance for optimizing consensus mechanisms in resource-constrained IoT-blockchain systems.