Z. Liang, Bin Chen, Litao Ye, Chen Sun · 6 authors
The ERC4907 standard enables rentable Non-Fungible Tokens (NFTs) but is limited to single-user, single-time-slot authorization, which severely limits its applicability and efficiency in decentralized multi-slot scheduling scenarios. To address this limitation, this paper proposes Multi-slot ERC4907 (M-ERC4907) extension method. The M-ERC4907 method introduces novel functionalities to support the batch configuration of multiple time slots and simultaneous authorization of multiple users, thereby effectively eliminating the rigid sequential authorization constraint of ERC4907. The experiment was conducted on the Remix development platform. Experimental results show that the M-ERC4907 method significantly reduces on-chain transactions and overall Gas consumption, leading to enhanced scalability and resource allocation efficiency.
Rutuja Chirwatkar, Beemkumar Nagappan, L. P. Singh, Anoop Dev · 6 authors
Another recent paradigm for enabling ubiquitous smart cities to be efficient, resilient, and innovative is Distributed Resource Management (DRM), in which heterogeneous devices, infrastructures, and services operate autonomously and continuously. This paper discusses more advanced concepts of edge-cloud synergy, decentralized coordination, cyber-physical integration, and context-aware optimization to address the increasing burden on urban energy, transportation, communication, and environmental systems. These are the main objectives: (1) to create scalable DRM frameworks with the features of real-time decision, (2) to enhance the interoperability of distributed heterogeneous resources, and (3) to enhance sustainability and service quality as a result of flexible allocation schemes. The proposed solutions will be the multi-agent systems, distributed ledger technologies (DLT), machine-learning-based prediction systems, and dynamic resource-orchestration algorithms. A hybrid simulation-prototype was applied to test the performance based on the metrics of latency, reliability, load balancing, and energy efficiency. Results suggest that significant improvements (up to a 35 percent reduction in resource contention, a 28 percent reduction in response time, and a 20 percent increase in system robustness under high-density urban workloads) have been achieved. A qualitative measure also fosters greater transparency and trust in cross-domain operations. In totality, the paper identifies that the disruptive potential of decentralized management systems can make smart-city ecologies adaptive, secure, and sustainable.
The Proof of Stake (PoS) consensus mechanism is increasingly used in blockchain systems; however, resource allocation for PoS-based mobile blockchain networks remains underexplored, particularly given the constraints of mobile devices. This work introduces MEC-Chain, a new framework that integrates Mobile Edge Computing (MEC) with mobile blockchain to support efficient validator-node execution under PoS. MEC-Chain formalizes a multi-objective resource-allocation problem that jointly considers latency, reliability, and cost from both the validator and MEC-provider perspectives. To address this challenge, we develop a deep reinforcement learning-based allocation agent using the Proximal Policy Optimization (PPO) algorithm. Experimental results show that PPO achieves a 30–40% reduction in total execution time, 25–35% lower transmission latency, and 10–15% higher reliability compared to A2C (Advantage Actor–Critic) and DQN (Deep Q-Network), while offering comparable cost savings across all methods. These results demonstrate the effectiveness of MEC-Chain in enabling low-latency, reliable, and resource-efficient PoS validation within mobile blockchain environments.
Federated learning (FL) enables collaborative model training across edge devices without centralizing raw data, but existing frameworks remain ill-equipped to support data privacy regulations mandated by GDPR, HIPAA, and CCPA. Once user data has influenced training, its verifiable removal becomes prohibitively expensive, particularly in non-IID and resource-constrained edge environments. This paper introduces a modular and scalable federated unlearning framework that unifies three complementary strategies: gradient subtraction, knowledge distillation, and checkpoint rollback, within an adaptive decision layer. A resource-aware checkpoint manager reduces storage costs through compression and pruning, while a privacy and trust layer integrates zero-knowledge proofs, differential privacy, and Merkle-based audit logs to provide verifiable guarantees of deletion. A non-IID-aware aggregator further preserves fairness across heterogeneous clients. Unlike prior approaches, our proposed framework systematically integrates rollback efficiency with formal privacy protections and auditability, offering a practical path toward trustworthy and regulation-compliant unlearning in domains such as healthcare, transportation, and smart agriculture.
Cloud computing has emerged as the dominant platform for contemporary data management and service provision. However, its centralized nature poses significant risks to security, privacy, and trust. Distributed systems can enhance data integrity and auditability by incorporating blockchain technology, which offers a decentralized and tamper-resistant approach. Nevertheless, the inherent transparency of blockchain conflicts with the confidentiality requirements of cloud environments. This review paper analyzes existing studies on privacy-preserving blockchain architectures designed to secure cloud-based information systems. A systematic literature review methodology was adopted, examining forty-eight peer-reviewed studies published between 2018 and 2024. The findings reveal that researchers have explored approaches such as encryption, zero-knowledge proofs, homomorphic encryption, and hybrid on/off-chain models to balance transparency and privacy. Scalability, interoperability, and regulatory compliance remain key challenges, particularly in permissioned blockchains, which nevertheless offer advantages in governance and compliance. The study identifies research gaps and future directions, including the development of common privacy frameworks, integration of confidential computing, and establishment of standardized evaluation metrics. Overall, privacy-sensitive blockchain architectures hold strong potential for creating trustworthy and secure cloud systems.
The increasing demand for real-time decision analytics in modern enterprises has accelerated the development of edge-to-cloud data pipelines, which integrate distributed computing resources to enable instantaneous insights. Traditional centralized cloud architectures struggle with latency and bandwidth limitations, making them unsuitable for applications requiring immediate decision-making. Edge-to-cloud pipelines overcome these barriers by combining localized data processing with cloud-based intelligence, creating a continuous, adaptive flow of analytical information. This review examines the architectural principles, technological enablers, and analytical impacts of edge-to-cloud data pipelines on real-time decision-making. It explores how distributed processing, stream analytics, and AI-driven orchestration enhance responsiveness, reliability, and scalability across diverse environments. Technologies such as 5G, machine learning, and containerized orchestration platforms are discussed as key drivers of this transformation. The study also identifies challenges including data synchronization, security, interoperability, and energy efficiency at the edge. Addressing these issues is essential for realizing seamless, end-to-end analytics across hybrid ecosystems. Future directions highlight the potential of autonomous, decentralized, and quantum-enhanced data pipelines to deliver self-optimizing intelligence at global scale.Ultimately, this review concludes that edge-to-cloud data pipelines are foundational to achieving context-aware, predictive, and autonomous analytics, enabling organizations to transition from reactive operations to real-time, intelligent decision ecosystems.
The purpose of this study is to address the persistent security and privacy challenges in cloud-based Electronic Health Record (EHR) sharing by proposing a blockchain-enabled architecture that integrates decentralized storage and smart contracts. Traditional mobile cloud solutions improve data accessibility but rely on centralized control, making them vulnerable to unauthorized access, single points of failure, and limited patient transparency. To overcome these limitations, this research designs a user-centric access control framework that leverages the Ethereum blockchain, smart contracts, and the InterPlanetary File System (IPFS) within a mobile cloud environment. The methodology involves developing and deploying a prototype on Amazon Web Services, supported by an Android-based mobile application that enables healthcare providers and patients to interact with the blockchain network. Experimental evaluation was conducted using wearable sensor data to test the performance, scalability, and resilience of the proposed system. The findings indicate that the framework ensures secure EHR exchange, enforces fine-grained access policies, and achieves reduced latency compared to conventional centralized approaches. Unauthorized requests were reliably detected and blocked through the smart contract mechanism, while authorized users accessed records with minimal delay. The results also confirm the lightweight overhead of the system, making it practical for mobile healthcare environments. The practical implications of this work lie in offering a tamper-resistant, transparent, and patient-centric solution for medical data sharing, thereby improving trust, reducing administrative overhead, and supporting real-time healthcare services in distributed and resource-constrained settings.
Shaista Ashraf Farooqi, Aedah Abd Rahman Rahman, Amna Saad
The growing integration of the Internet of Medical Things (IoMT) into healthcare has amplified the need for secure and privacy-preserving artificial intelligence. Federated Learning (FL) has emerged as a pivotal paradigm for decentralized medical data processing; however, it still faces challenges concerning data confidentiality, trust management, and scalability. This review presents an extended theoretical comparison of two prominent privacy-preserving frameworks—Federated Learning with Differential Privacy (FL-DP) and Federated Learning with Blockchain (FL-BC)—to assess their suitability for ensuring data security, transparency, and regulatory compliance in IoMT environments. The FL-DP framework safeguards patient data through noise injection during model updates, offering mathematically proven privacy guarantees. Conversely, the FL-BC framework reinforces trust and integrity via immutable ledgers and consensus mechanisms such as Proof of Stake (PoS) and Byzantine Fault Tolerance (BFT). Reviewing literature published between 2021 and 2025, this study examines trade-offs in privacy, scalability, latency, and energy efficiency, while highlighting emerging hybrid architectures that integrate both approaches. The findings reveal that FL-DP provides stronger privacy control, whereas FL-BC ensures verifiable trust and traceability—together forming the foundation for next-generation secure and trustworthy federated learning systems in IoMT-driven healthcare.
Aleksei Olkhovikov, Yash Madhwal, Arsen Andrian, Hamza Imran · 8 authors
• Prototype system with Raspberry Pi and dual ultrasonic sensors for data acquisition. • Real-time data signing and blockchain submission using web3.py and EVM chain. • Smart contract for secure data logging, access control, and gas-efficient events. • Frontend with Streamlit MVP and Vue3 dashboard supporting secure user login. • Experiments on 15M-record dataset to evaluate gas cost, batching, and scalability. Integrity and traceability of sensor data in oilfield operations are essential for safe, efficient, and compliant resource extraction. This paper presents a blockchain-enabled proof-of-concept (PoC) IoT framework that facilitates decentralized, tamper-evident monitoring of oil extraction infrastructure. The system integrates field-deployed sensors with a Raspberry Pi-based edge controller to capture, buffer, and cryptographically sign telemetry data, which is then submitted to an EVM-compatible blockchain using smart contracts. The PoC demonstrates historical and real-time data visualization through a web-based dashboard that authenticates and displays blockchain event streams. A real-world drilling data set comprising more than 15 million records is used for the experimental evaluation of the prototype. Gas consumption metrics are analyzed under varying payload sizes and batching strategies, revealing linear scalability with respect to parameter volume and significant efficiency gains through transaction batching. These results demonstrate measurable improvements in resource utilization and operational cost, confirming the framework’s efficiency and robustness for large-scale industrial telemetry. The architecture supports secure access control, structured metadata annotation, and transparent logging without reliance on centralized intermediaries. By addressing key challenges in data authenticity and operational visibility, the proposed solution establishes a scalable foundation for secure telemetry in oil and gas operations, with potential applicability to other critical infrastructure domains such as energy grids, mining, and water resource management. Unlike prior blockchain-IoT frameworks focusing primarily on architectural design or off-chain coordination, the proposed system demonstrates an end-to-end implementation directly linking field-level sensors to on-chain storage and visualization. Through large-scale validation on a 15 M-record drilling dataset, this work provides one of the first empirical analyses of gas-efficient, real-time telemetry submission in industrial settings.
The fast growth of Internet of Things (IoT) technologies has turned smart cities into big data ecosystems for intelligent mobility, energetic efficiency and public services. But this increasing reliance on IoT data raises significant privacy issues because of the perpetually gathered sensor readings, inter-organisational sharing and algorithmic analyses. In this paper, we focus on the state-of-the-art IoT data-sharing methods that preserve privacy and protect recent progress in preserving privacy while sharing data in the IoT personal record by preserving statistical value. It combines traditional approaches, including anonymisation, differential privacy, federated learning, secure multiparty computation and homomorphic encryption with new technologies (e.g., blockchain-enabled governance, edge intelligence or zero-knowledge proofs) (Nguyen et al., 2023; Alrawais et al., 2024; Lin & Kuo, 2025). The paper analyses the impact of hybrid architectures combining edge-cloud cooperation and decentralised access control for improving data protection, in terms of not losing performance or interoperability. Conclusions: Summary of the main findings, Barriers to Implementation. This paper identifies several ongoing barriers, including computational expense, related to past research. Personal data while preserving its analytical worth. It combines cutting-edge technologies like blockchain-enabled governance, edge intelligence, and zero-knowledge proofs with traditional strategies like anonymisation, differential privacy, federated learning, secure multiparty computation, and homomorphic encryption (Nguyen et al., 2023; Alrawais et al., 2024; Lin & Kuo, 2025). The study investigates how hybrid architectures that incorporate decentralised access control and edge-cloud collaboration can improve data security without compromising interoperability or performance. The results point to enduring obstacles, such as interoperability, computational overhead, and regulatory compliance, especially in urban settings with limited resources. In order to integrate privacy-by-design principles into IoT analytics for smart city governance, the study suggests a multi-layered conceptual framework. To maintain public confidence in urban digital transformation, this framework places a strong emphasis on open data policies, citizen consent procedures, and the incorporation of cutting-edge cryptographic techniques. The information adds to the current discussion on how to balance privacy and innovation in smart cities and provides guidance for system architects, legislators, and municipal IT leaders who want to adopt IoT responsibly.
The rapid expansion of Internet of Things (IoT) devices poses significant challenges for traditional centralized identity and access management (IdM) systems, which suffer from scalability limitations, single points of failure, and notable privacy risks. Although blockchain technology presents a promising decentralized solution, its direct adoption is often constrained by limited transaction throughput, high operational costs, and the computational constraints of IoT devices. To address these issues, this study proposes and rigorously evaluates HybID-AC, a novel hybrid architecture for decentralized identity and access management, specifically designed for large-scale, heterogeneous IoT ecosystems. HybID-AC employs a dual-layer design that separates global trust anchoring from local execution. A highly scalable, feeless Directed Acyclic Graph (DAG)-based distributed ledger functions as a public anchor layer, registering W3C-standard Decentralized Identifiers (DIDs) and access policy hashes. High-frequency access control operations are handled off-chain at the edge layer, leveraging the DIDComm v2 peer-to-peer protocol, Attribute-Based Access Control (ABAC) for fine-grained policy enforcement, and Zero-Knowledge Proofs (ZKP) to preserve attribute privacy. Analytical results demonstrate that the HybID-AC architecture significantly improves latency and cost-efficiency compared to fully on-chain approaches, maintaining stable performance even as network scale increases. Additionally, a novel probabilistic model is introduced to provide a quantitative measure of the integral security risk of ABAC policies under potential attribute compromise. Overall, the study concludes that this hybrid architecture effectively addresses the inherent trade-offs of blockchain in IoT systems, delivering a secure, scalable, and interoperable framework that empowers devices with self-sovereign identity while ensuring privacy and security by design.
Decentralized multi-agent systems have shown promise in enabling autonomous collaboration among LLM-based agents. While AgentNet demonstrated the feasibility of fully decentralized coordination through dynamic DAG topologies, several limitations remain: scalability challenges with large agent populations, communication overhead, lack of privacy guarantees, and suboptimal resource allocation. We propose AgentNet++, a hierarchical decentralized framework that extends AgentNet with multilevel agent organization, privacy-preserving knowledge sharing via differential privacy and secure aggregation, adaptive resource management, and theoretical convergence guarantees. Our approach introduces cluster-based hierarchies where agents self-organize into specialized groups, enabling efficient task routing and knowledge distillation while maintaining full decentralization. We provide formal analysis of convergence properties and privacy bounds, and demonstrate through extensive experiments on complex multi-agent tasks that AgentNet++ achieves 23% higher task completion rates, 40% reduction in communication overhead, and maintains strong privacy guarantees compared to AgentNet and other baselines. Our framework scales effectively to 1000+ agents while preserving the emergent intelligence properties of the original AgentNet.
There has been an exponential rise of Internet of Things (IoT) devices and autonomous systems, which have thrown light on the weaknesses of centralized cloud computing, especially in latency, bandwidth, and security. This paper will solve such problems by suggesting an integrated blockchain-edge architecture, which uses distributed trusting mechanisms to protect and optimize edge networks. The process of the methodology consists of four steps: architectural modeling, lightweight consensus design, performance-security trade-off analysis, and real-life validation. Experiments with iFogSim and BlockSim showed that edge networks enhanced with blockchain cuts latency and bandwidth consumption by 37 and 36 percent respectively compared to cloud-centric models. Consensus protocols such as Practical Byzantine Fault Tolerance (pBFT), Proof-of-Elaboration (PoE) and Leased Proof-of-Stake (LPoS) were designed and tested, using much less energy and having much faster transaction finality compared to Proof-of-Work. High resilience to Sybil, tampering, and 51% attacks was proven with Raspberry Pi clusters, and an 8% latency trade-off was observed, when smart contracts were used to enforce automated access control. Lastly, experimental validation with healthcare and industrial IoT datasets demonstrated that blockchain decreased attempts to access information unauthorized to nearly zero in the healthcare industry and minimized manipulations with machine logs by 70 percent in the industrial IoT. These results highlight blockchain-edge convergence as a potential direction towards the construction of scalable, secure and trustful decentralized systems.
The convergence of artificial intelligence (AI) and decentralized web technologies represents a pivotal shift in digital infrastructure, giving rise to the concept of AI-native protocols. These protocols integrate AI capabilities directly into their fundamental design, moving beyond mere application-level AI to create intelligent, adaptive, and autonomous decentralized systems. This paper explores the transformative potential of AI-native protocols in reshaping the decentralized web, often referred to as Web3. We delve into the architectural implications, key benefits such as enhanced security, efficiency, and scalability, and the profound societal impact of such a paradigm shift. Through a comprehensive literature review, we identify existing challenges in both AI and blockchain domains that AI-native protocols are uniquely positioned to address, including algorithmic bias, data privacy, and consensus mechanism inefficiencies. We propose a conceptual framework for designing these protocols, emphasizing core components like intelligent consensus, autonomous agents, and AI-powered smart contracts. Furthermore, the paper discusses the ethical considerations inherent in embedding AI within decentralized governance structures and outlines future research directions for fostering responsible innovation. Our findings suggest that AI-native protocols are not merely an incremental improvement but a foundational evolution that promises to unlock unprecedented levels of intelligence and autonomy across the decentralized digital landscape, fostering a more robust, equitable, and resilient internet.
Sasikumar Asaithambi, Sunil Prajapat, Mohammed Wasim Bhatt, Syed Rizwan Hassan
ABSTRACT The Internet of Things (IoT) can offer more precise, intelligent, and low‐ or non‐human involvement approaches to various sectors. One of the significant uses of the IoT is in smart cities, which includes a variety of services, including smart home, garbage disposal, and smart grid. Many different collaborative IoT integrations are available in smart cities because of these diverse services. A digitized smart city was created to offer complete government cooperation solutions based on digitization and automation to improve residents' quality of life. Information safety and privacy concerns arise when various services need to work together seamlessly. Trustworthy data is vital to the federal government and its constituents, and data accuracy and privacy must be ensured. In this work, we presented a smart contract‐enabled smart city and software‐defined networking (SDN) in limited contexts during collaborative activities based on a controlled network and decentralization. The proposed collaborative application safety structure is being tested on the Hyperledger blockchain networks. We describe a unique approach to data security through collaborative work in intelligent city governmental design, utilizing Proof‐of‐Trust Collaboration (PoTC) in Hyperledger blockchains. A security approach based on SDN and smart contracts is employed to safely manage and monitor all connections and transactions across diverse IoT networks. To assess the viability of the proposed decentralized security framework, we created a supported scenario for collaborative activities in an SDN‐enabled IoT design. We have conducted various experimental simulations to test the proposed blockchain‐integrated SDN‐based IoT architecture for a smart city, including throughput, access delay, and trust evaluation. The simulation results show that the proposed SDN‐enabled blockchain networks provide better results than existing works.
ABSTRACT The combination of blockchain technology with Industrial Internet of Things (IIoT) frameworks is promising in terms of building trust, data authenticity, and resilience. However, the efficiency and feasibility of integration largely rely upon the consensus mechanisms used. The present study is an overview of four renowned blockchain consensus schemes, namely Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Delegated Proof of Stake (DPoS), and the corresponding performance, security, efficiency, as well as compatibility under IIoT. The results reveal that low‐latency and lightweight consensus, such as PBFT and DPoS, will be helpful in IIoT applications, especially in applications with scarce resources. The paper offers practical guidance on the development of IIoT systems with integrated blockchain customized based on the requirements of the industry.
Self-collecting Internet of Things (IoT) gadgets have transformed healthcare systems. Centralising IoT healthcare data processing and storage introduces scalability, speed, security, and privacy issues. On the other hand, Blockchain technology attracts interest in the IoT healthcare industries because of its decentralisation, data protection, transparency, and security aspects. Single public blockchain ledgers are inefficient for healthcare IoT security and efficiency due to high transaction fees, limited scalability, and high patient traffic. Specifically, this article focuses on the concerns around privacy, security, performance, scalability, and energy consumption in healthcare blockchain-IoT systems. In this paper, we propose CareChain, an IPFS storage system with two blockchains, one for patients and the other for healthcare providers, to manage healthcare IoT data. The proposed model uses IPFS distributed storage to improve system throughput, reducing transaction latency and blockchain storage overhead. It improves storage requirements, energy efficiency, transaction speed, privacy, and security. It envisions a system-wide data and information security architecture that uses the Elliptic Curve Digital Signature Algorithm (ECDSA) and a device proxy to keep tabs on low-cost devices. The prototype model was tested to investigate its security, efficiency, and energy use. The results show that this system is more robust than the existing healthcare models.
K. Swathi, Putta Durga, K. Venkata Prasad, A Krishna Chaitanya · 7 authors
An enormous demand for a secure, scalable, intelligent edge computing framework has emerged for the exponentially increasing number of Internet of Things (IoT) devices for any substrate of modern digital infrastructure. These edge nodes distributed across heterogeneous environments serve as primary interfaces for sensing, computation, and actuations. Their physical deployment in unattended scenarios puts them at risk of being targets for resource manipulation. One widely accepted IoT architecture with traditional notions of edge may consider a threat to its centralized knowledge with an unbounded attack surface that includes anything that can remotely connect to the edge from the cloud-like domain. Existing strategies either forget the dynamic risk context of edge nodes or do not achieve a reasonable trade-off between security and resource constraints, essentially degrading the robustness and trustworthiness of solutions intended for real-life scenarios. To address the existing gaps, the work presents a novel Blockchain Integrated Deep Learning Framework for secure IoT edge computing, introducing a hybrid architecture where the transparency of blockchain meets deep learning flexibility. The proposed system incorporates five specialized components: Blockchain-Orchestrated Federated Curriculum Learning (BOFCL), which ensures risk-prioritized training using threat indices derived from blockchain logs; this adaptive sequencing enhances responsiveness to high-risk edge scenarios. Zero-Knowledge Proof Enabled Secure Inference Engine (ZK-SIE) provides verifiable privacy-preserving inference, ensuring model integrity without exposing input data or model internals in process. Blockchain Indexed Adversarial Attack Simulator (BI-AAS) focuses on testing the models in edge environments against attack scenarios drawn from common adversarial profiles and thereby facilitates a model defensive retraining. Energy-Aware Lightweight Consensus with Adaptive Synchronization (ELCAS) avoids overhead by seeking energy-efficient participants for global model synchronization in constrained environments. Trust Indexed Model Provenance and Deployment Ledger (TIMPDL) ensures model lineage tracking and deploy ability in a transparent manner by providing composite trust scores computed from data quality, node reputation, and validation metrics. Altogether, the framework combines the data integrity, adversarial robustness, and trust-aware deployment, shortening training latency, synchronization energy, and privacy leakage. It is a foundational advancement supporting secure decentralized edge intelligence for next-generation IoT ecosystems.
N. A. Natraj, J. Midhunchakkaravarthy, Brojo Kishore Mishra
INTRODUCTION: The application of blockchain technology to Internet of Things (IoT) systems offers substantial potential for enhancing security, but traditional consensus mechanisms are ill-suited for resource-constrained environments. While hybrid consensus solutions have emerged as a promising alternative, a systematic framework for their classification and evaluation is notably absent. OBJECTIVES: This study addresses this critical gap by introducing a novel, application-driven framework for analyzing hybrid consensus mechanisms, underpinned by a quantitative synthesis of performance benchmarks. METHODS: We analyze diverse architectures—including combinations of Proof of Work (PoW) and Proof of Stake (PoS), PBFT-enhanced systems, and hierarchical models—through the lens of specific IoT application priorities, such as latency, energy efficiency, and scalability. Case studies of IOTA's Tangle, IoTeX's Roll-DPoS, and Hyperledger Fabric illustrate these practical trade-offs. RESULTS: Our framework reveals not only primary performance trade-offs but also critical "second-order" complexities, such as emergent vulnerabilities at the intersection of different consensus layers. CONCLUSION: Our findings demonstrate that this structured, quantitatively-grounded approach provides an effective methodology for designing and selecting regulatory-compliant hybrid consensus solutions for specific IoT applications.
Time-sensitive Internet of Things (IoT) deployments need fine-grained, auditable authorisation without exposing payloads to intermediaries or embedding access policy in cipher-text. The Secure IoT Communication and Policy Enforcement (SCOPE) framework separates on-ledger authorisation from end-to-end content protection while keeping intermediaries minimally trusted. The SCOPE framework comprises a Broker Smart Contract (BSC) that records authorisation decisions, a decentralised Trusted Authority (TA) that issues committee attestations and epoch-scoped revocation snapshots, and a stateless edge relay that verifies requests and forwards ciphertext without decryption. Payload confidentiality and integrity are provided end-to-end by a pairing-free authenticated encryption with associated data (AEAD) channel with ephemeral key agreement and disciplined nonces, yielding replay resistance and forward secrecy with respect to the sender’s key. A prototype on a permissioned distributed ledger runtime, evaluated on an IoT edge testbed, demonstrates sub-second end-to-end operation, on-ledger authorisation within 500 ms, and lower computational latency than pairing-based Ciphertext-Policy Attribute-Based Encryption and Attribute-Based Signcryption (CP-ABE/ABSC) baselines, including BLUMA (multi-authority CP-ABE with hidden policy). The design is portable across ledgers and supports a drop-in post-quantum key-encapsulation mechanism plus AEAD (KEM+AEAD) channel without changes to the policy or relay planes, enabling auditable authorisation for multi-stakeholder settings such as smart ports, industrial automation, and e-health.
Ravi Khile, Shravani Karvande, Pradnya Katbane, Saniya Shaikh
Water quality degradation has become a pressing global challenge due to rapid industrialization, urbanization, and population growth. Conventional water quality monitoring systems rely on manual testing or cloud-based IoT frameworks, which often face vulnerabilities such as data tampering, network latency, and security breaches. To address these limitations, this research proposes a Blockchain-Based Secure Data Framework for IoT Water Monitoring using ESP32 and LoRa communication integrated with Firebase Cloud. The proposed system ensures tamper-proof, transparent, and decentralized data management for multi-parameter water quality monitoring. IoT sensor nodes equipped with pH, turbidity, TDS, and temperature sensors collect real-time data transmitted via LoRa gateways to a blockchain-enabled cloud interface. The blockchain layer secures sensor data through cryptographic hashing, consensus validation, and distributed ledger mechanisms. Experimental validation demonstrates that blockchain integration reduces unauthorized data manipulation by 98% and enhances system trust and traceability. The framework achieves an average latency of 1.2 seconds per transaction and consumes 27% less power compared to traditional cloud-only solutions. The results highlight blockchain’s potential to revolutionize secure environmental monitoring and ensure reliable, transparent water data management for sustainable smart cities.
The Internet of Things (IoT) generates massive volumes of patient and healthcare data every day. Providing the required accuracy for data classification, processing time, and analyzing the vast volumes of data from IoT devices and sensors are said to be the main challenges in IoT. Cloud computing is widely utilized as the foundation for the technologies required to secure healthcare. The healthcare industry has the most promise for blockchain technology since it can be used to integrate fragmented systems, the standard of electronic medical records should be raised, and take a more patient-centric approach to healthcare systems. The objective is to protect medical data, enable patients to use it to support their medical care, and provide reliable consent protocols for data exchange between various institutions and apps. Provide a blockchain-based architecture that verifies user identity using the Secure Hash Algorithm (SHA256) and Proof of Stake (POS) cryptography consensus technique to guarantee EHR sharing across many electronic healthcare systems.In this study, we assessed the performance of our proposed architecture using several metrics, and found that blockchain is a reliable security solution for the upcoming IoT network.
In complex environments such as those incorporating distributed and edge computing, middleware plays a critical role in meeting the communication and performance requirements of distributed systems by providing communication flow and integration capabilities. Its inherent advantages, such as abstraction of complexities, enhanced interoperability and scalability, make it ideal for managing tasks such as federated learning in edge AI environments. In addition, by supporting secure and energy-efficient operations, the middleware fosters sustainability, enabling green blockchain solutions and low-power distributed ledger technologies (DLTs) to thrive for managing dynamic ecosystems such as dAIEDGE. This deliverable D5.3, "Middleware prototype" presents the first version of dAIEDGE middleware. This work has been developed during the first year of dAIEDGE project from M4 to M16. In general, the document outlines the first version of the middleware developed collaboratively with task partners, by the University of Salamanca (USAL) as part of Task T5.2, "Middleware and Networks for Edge AI," within the dAIEDGE project. This task reflects a joint effort involving multiple participants, including BCA, BTH, CETIC, KUL, VICOM, and UEDIN.
Electronic Health Records (EHR) is the main core of modern healthcare, but interoperability across different blockchain platforms is a key challenge. This work proposes a cross-chain middleware architecture, which facilitates secure and real-time synchronization of EHR data between Hyperledger Fabric (private blockchain) and Ethereum Sepolia Testnet (public blockchain). The framework integrates AES-256 encryption and Inter Planetary File System (IPFS) as decentralized storage to enhance patient privacy. To facilitate interoperability across the blockchains the research introduces a smart middleware layer. This layer autonomously monitors the blockchain events, processes encrypted CIDs, enforces real time cross chain consistency and smart contract-based access control. The experimental evaluation shows that proposed framework achieves low synchronization times (< 195 ms), low gas and latency costs, small encryption overhead (< 4–5 KB), robust file storage and retrieval through IPFS. Such positive evaluations with scalable and real-time deployment, sets the foundation of patient centric interoperable healthcare ecosystems.