The exponential proliferation of Internet of Things (IoT) devices presents critical challenges to traditional centralized identity and access management systems, which are plagued by issues of scalability, single points of failure, and significant privacy risks. While blockchain technology offers a promising decentralized alternative, its direct application is often hindered by low transaction throughput, high costs, and the computational limitations of IoT devices. This study addresses these challenges by proposing and formally evaluating HybID-AC, a novel hybrid architecture for decentralized identity and access management tailored for large-scale, heterogeneous IoT ecosystems. The methodology involves a dual-layer design that separates global trust anchoring from local execution. A highly scalable, feeless Directed Acyclic Graph (DAG) based distributed ledger serves as a public "anchor layer" for registering W3C standard Decentralized Identifiers (DIDs) and access policy hashes. All high-frequency access control operations are processed off-chain at the "edge layer" using the DIDComm v2 peer-to-peer protocol, Attribute-Based Access Control (ABAC) for fine-grained policy enforcement, and Zero-Knowledge Proofs (ZKP) to ensure privacy-preserving attribute verification. The results of our analytical evaluation demonstrate that the HybID-AC architecture achieves orders-of-magnitude improvements in latency and cost-efficiency compared to fully on-chain models, maintaining consistent performance as the network scales. Furthermore, we introduce an original probabilistic model that provides a quantitative metric for assessing the integral security risk of ABAC policies against attribute compromise. The study concludes that this hybrid approach effectively resolves the inherent trade-offs of blockchain in an IoT context, offering a robust, scalable, and interoperable framework that empowers devices with self-sovereign identity while ensuring security and privacy by design.
In this paper, a novel security framework for industrial internet of things (IIoT) and vehicular networks is proposed, integrating blockchain technology with advanced encryption and data classification mechanisms to enhance data integrity, confidentiality and trustworthiness. The work employed ChaCha20-Poly1305 encryption to safeguard the data transaction to local cluster nodes. A private blockchain gateway then processes the encrypted data, classifying it based on confidentiality levels, and directing storage either to cloud servers or the interplanetary file system (IPFS). To ensure data integrity, a proof of authority consensus mechanism within the blockchain is incorporated, while zero knowledge proof (ZKP) methods are used for authentication and secure data access. Empirical evaluations demonstrate that our framework achieves a data transmission security rate of 97.5%, with an average encryption and decryption latency of 150 milliseconds, significantly improving over traditional methods. The proof of authority consensus mechanism exhibits a transaction validation speed of 300 transactions per second, showcasing enhanced efficiency compared to standard blockchain models. Furthermore, the integration of ZKP challenges results in a 30% reduction in unauthorised access attempts, indicating a substantial improvement in overall security. This work emphasises the need for continuous innovation in addressing the various security issues in IoT, ultimately advancing the operational efficiency and security of these systems.
Federated learning represents a paradigm shift in distributed machine learning by enabling collaborative model training across decentralized nodes while maintaining data privacy at source locations. It helps bridge the gap between artificial intelligence-driven development guidelines and the regulatory mandates laid down by data protection legislation. A decentralized architecture transmits only the model updates to aggregation servers; this reduces privacy breach exposure and compliance violation risks and also eliminates raw data centralization. Federated learning helps build production-ready systems across healthcare, finance, and edge computing environments, owing to the maturities that have occurred in cloud infrastructure. This is a transition from the erstwhile theoretical frameworks it used to have. Architectural advantages are supplemented by privacy-preserving mechanisms like differential privacy and secure aggregation protocols, which facilitate organizations to leverage collective intelligence without exposing sensitive information. Robust platforms for privacy-critical applications can be synthesized by the integration of cloud-native security services, cryptographic enhancements, and edge computing optimization. Courtesy of emerging solutions that cater to model fairness, communication efficiency, and data heterogeneity, federated learning's practical applicability across diverse organizational contexts and regulatory domains continues to advance.
Rana Hassam Ahmed, Muhammad Sarfraz Khan, Amirmohammad Delshadi, Naseer Ahmad · 5 authors
Internet of Medical Things (IoMT) provides the possibility to conduct continuous monitoring of health, perform intelligent diagnostics, and make a clinical decision based on data. Nonetheless, there are security, privacy, scalability, latency, and energy issues with large-scale deployment. Although Federated learning (FL) provides less exposure to data, and blockchain provides trust, current solutions that combine both blockchain and FL have high consensus overhead, fixed privacy, and adversarial resilience. To handle them, we present an Edge-Intelligent Hierarchical Blockchain-IoMT framework that integrates Hierarchical FL (HFL), Adaptive Differential Privacy (ADP), Lightweight Homomorphic Encryption (LHE), Zero-Knowledge Proof (ZKP) authentication, and an Energy-Aware PoS with Edge Learning (PoS-EL) consensus. Hierarchical aggregation minimizes bottlenecks in communication. ADP minimizes security vs utility. ZKP achieves authentication and PoS-EL minimizes energy consumption. Experiments on real-world data demonstrate 99.21% accuracy of detecting anomalies, 34% decreased latency, 41% decreased energy usage, 52 percent lower blockchain overhead and 97 percent resistance to adversarial attacks, which justifies the framework in real-time, mission-critical IoMT systems.
The advent of 5G networks has introduced a paradigm shift in communication infrastructure, facilitating ultra-low latency and high-speed data transmission. Despite this, this progress is accompanied by a spike in diverse and sophisticated cyberattacks, for which there is no comprehensive, foolproof defence strategy. In order to address the Scalability Trilemma—achieving decentralization, scalability, and trust—and security concerns, this study proposes a robust security framework that combines blockchain technology with Zero Trust Architecture (ZTA). The proposed framework presents an end-to-end coherent workflow in four successive stages: (i) Access Request Initiation with contextual metadata, (ii) Decentralized identity verification via blockchain-based Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs), (iii) Context-aware Dynamic Access Control enforced through smart contracts, risk scoring, and cryptographic mechanisms such as Zero Knowledge Proofs (ZKPs) and Multi-Factor Authentication (MFA), and (iv) Time-bound, least-privilege access provisioning with continuous session monitoring and immutable logging. The model, which is proposed to be strategically implemented at the 5G network's device (access) layer, affirms real-time enforcement while maintaining accountability, privacy, and verifiability. Our research delivers a fully decentralized, tamper-resistant, and scalable architecture capable of dynamically mitigating advanced cyber threats, while ensuring secure delivery of 5G services across diverse use cases.
: Public blockchains enable decentralized applications but continue to face persistent challenges in scalability, privacy, and decentralization. This survey employs a structured and comprehensive literature review of 114 peer-reviewed studies and reputable technical reports (2018–2025), selected using predefined search strings, inclusion/exclusion criteria, and a structured screening process, documented using a Literature selection flow diagram. Scalability techniques—including sharding, Layer 2 architectures (e.g., ZK-Rollups, Optimistic Rollups, commit chains), and privacy-enhancing technologies such as zero-knowledge proofs (ZKPs), trusted execution environments (TEEs), and protocol-native mixers—are critically analyzed. Standardized benchmarking evaluates throughput, latency, gas efficiency, and decentralization under consistent test conditions. A key contribution of this study is the first integrated, datadriven assessment of privacy–scalability trade-offs within the blockchain scalability trilemma framework. Empirical benchmarking indicates, for example, that zkSync Era demonstrates a theoretical throughput of ~2000 TPS but achieves ~0.52 TPS under measured network conditions, highlighting the computational overhead of ZKP generation. Hybrid architectures—such as zkPorter’s off-chain data availability combined with ZKPs or TEE-based routing— consistently outperform single-layer approaches in balancing performance, confidentiality, and trustlessness. Post-2021 advancements, including modular rollups, MEV-resistant sharding, and machine-learning-based load prediction, are reviewed alongside open challenges in standardized benchmarking, post-quantum privacy systems, and compliance-aware PETs. Future research directions emphasize cross-layer designs integrating ZKPs, dynamic sharding, and regulatory- ready privacy protocols to enable secure, scalable, and legally compliant blockchain ecosystems.
The rapid convergence of the Internet of Things (IoT) and decentralized finance (DeFi) is reshaping the digital economy by enabling autonomous, trustless, and value-driven interactions among connected devices. This paper provides a comprehensive survey of the emerging paradigm that combines IoT's pervasive sensing and communication capabilities with DeFi's programmable financial infrastructure. We first discuss the motivation behind this convergence and explore key opportunities, including autonomous machine-to-machine (M2M) payments, decentralized data marketplaces, and trustless IoT service provisioning. Despite its potential, IoT-DeFi integration introduces significant security and privacy challenges related to smart contract vulnerabilities, consensus protocol risks, oracle manipulation, and constrained device capabilities. We review existing mitigation approaches such as lightweight cryptography, secure contract design, and decentralized identity management, and critically assess their limitations in heterogeneous, resource-limited environments. Building on this analysis, identify research gaps and propose future directions emphasizing formal verification of IoT-integrated smart contracts, robust oracle design, interoperability frameworks, and privacy-preserving trust models. This survey systematically maps opportunities, threats, and open issues. In doing so, it guides researchers and practitioners toward building secure, scalable, and energy-efficient IoT-DeFi ecosystems for next-generation decentralized applications.
The rapid integration of Cloud computing and the Internet of Things (IoT) has enabled large-scale data storage, real-time analytics, and automation across a wide range of applications. Nonetheless, centralized architectures in Cloud-IoT integrations are subject to serious issues related to data security, trust, transparency, and automated access control. This paper introduces a smart contract framework based on blockchain to distribute data and implement a computerized system in Cloud-IoT securely. The proposed solution can be achieved through blockchain, which provides a decentralized, tamper-resistant architecture, thereby guaranteeing data integrity, non-repudiation, and a clear transaction log between heterogeneous IoT devices and Cloud services. Smart contracts enforce policies of data sharing, authentication, authorization, and service-level agreements without using trusted third parties. The framework enables access control at a fine-grain scale and dynamic policy enforcement, which allows a safe and effective process of data exchange in multi-stakeholder ecosystems. Moreover, the off-chain Cloud storage with on-chain verification will resolve the issues of blockchain scaling and storage capacity and ensure security assurances. A qualitative relationship results in the proposed architecture taking a long step in enhancing trust, minimizing operational overhead, and limiting the common security threats, including data manipulation, unauthorized access, and single points of failure. The proposed solution provides a strong foundation for next-generation Cloud-IoT systems in applications such as smart cities, healthcare, industrial automation, and intelligent transportation systems.
The most important problems of sustainable vegetable farming are pests, inefficient data-based decision making, and poor irrigation. Another threat to productivity and the environment is that the conventional methods will cause excess use of pesticides, over-irrigation and unreliable harvest. In this research, we suggest a Blockchain Ethereum-Backed IoT Platform to ensure that these problems are resolved and provide pest detection in real-time and smart irrigation control. IoT sensors are used to measure soil moisture, temperature, and humidity and edge devices with lightweight deep learning models can detect pest infestations with a high accuracy. The resulting data is encrypted and checked in the Ethereum blockchain smart contracts are used to run irrigation programs and send alerts to control pests. It uses Layer-2 solutions of Ethereum to reduce latency and transaction cost to achieve scalability and efficiency. It is scientifically proven that the proposed platform can detect pests with an accuracy of 93.8 %, use 35 % less water, and produce 20% more crops than traditional solutions. Moreover, surveys of farmers show that the level of trust and readiness to implement solutions based on blockchain has risen considerably. The contribution of this work is a secure and transparent and resource-efficient digital agriculture framework enabling the development of precision-based farming and supporting sustainable food production.
Blockchain technology has evolved incredibly into various domains other than cryptocurrencies such as healthcare, genomics application, agriculture, government schemes, land asset distribution, DeFi, IoT, supply chain management due to its decentralized and secured nature. Consensus mechanism in blockchain networks serves as the backbone to ensure data integrity, provenance, immutability and security. Traditional consensus mechanism faces many challenges like utilization of high energy or carbon, excessive computational resources, staking of cryptocurrency, high reputation of nodes, maximum votes received, scalability and security issues. To tackle this concerns many researchers has proposed solutions and given a comparative analysis of the performance of these algorithms. This paper gives the survey reviews of the consensus mechanism used so far with a comparative analysis on the performance metrics like scalability, latency, and throughput, degree of decentralization, energy and resources efficiency etc. We have divided the consensus algorithms based on two categories i.e Proof based and Acquiescence based. The study highlights critical trade-offs among scalability, energy efficiency, decentralization, fault tolerance, and security resilience. Furthermore, this paper sheds the light on recent innovations addressing mitigation strategies like sharding, off-chain solutions, checkpoint mechanism, and integration of machine learning for anomaly detection, prediction of attack vectors. By systematically comparing consensus protocols and identifying open research challenges, this review aims to provide researchers and practitioners with a clear understanding of current consensus landscapes and provide valuable guidance to the selection and design of suitable mechanisms for next-generation blockchain systems.
Siri Sanjana Pasunoori, Swetha U, Ravi Kanth Kotha, Kumar Dorthi · 7 authors
The increasing integration of distributed energy resources (DERs) into modern smart grids has created new challenges related to load balancing, real-time coordination, and secure energy transactions. Traditional centralized grid architectures are no longer sufficient to handle bidirectional energy flow, dynamic pricing, and operational requirements. The current paper proposes a scalable smart grid load balancing framework by integrating lightweight Message Queuing Telemetry Transport (MQTT) communication with a hybrid blockchain-based consensus mechanism. Practical Byzantine Fault Tolerance (PBFT) and Proof of Stake (PoS) were used to achieve consensus. MQTT provides low-latency and efficient communication among prosumer devices. And the blockchain layer ensures secure, tamper-evident, and auditable power transactions. The proposed hybrid consensus model achieves quicker transaction finality and byzantine fault tolerance within local microgrids and supports a scalable and economically secure environment through PoS. Smart contracts were utilized to automate important functions such as settlement, marginal pricing, and bid matching. The simulation outcome shows communication latency within a second, around 85% prosumer participation in demand response programs, and also a 23% increase in renewable energy utilization. The proposed framework provides a secure, transparent, and interoperable solution for next-generation decentralized smart grid systems.
Mar 2, 2026·Proceedings of the 2026 International conference on Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) solutions for Europe's Next-Gen Cloud Infrastructure
Nicola Bicocchi, Enrico Rossini, Marco Picone, Marco Mamei
The IoT-Edge-Cloud Continuum (IECC) demands data architectures capable of handling heterogeneity, distributed ownership, and governance across diverse stakeholders. This paper examines the combined use of Data Mesh and Data Spaces as complementary paradigms for addressing these challenges. Data Mesh decentralizes data management and computation across domains through autonomous data products; Data Spaces provide the trust, semantics, and policy frameworks required for sovereign and interoperable data exchange across organizations. Within the Horizon Europe NOUS project, we integrate these paradigms to form a knowledge-centric computing continuum. Using the Modena Automotive Smart Area (MASA) as a real-world testbed, we show how this integration supports scalable, trusted, and semantically aligned intelligence for smart mobility applications.
The growing implementation of blockchain technology across various application areas has made the need for energy-efficient and scalable consensus mechanisms more pressing. Conventional consensus protocols like Proof of Work (PoW), although secure in nature, have high energy expenditures and are limited in scalability. This paper introduces a new hybrid consensus algorithm that merges Proof of Stake (PoS) and Proof of Elapsed Time (PoET) to overcome such limitations. The new method leverages the deterministic stake-based leader election of PoS and the low-energy time-based leader election facilitated by PoET’s utilization of Trusted Execution Environments (TEEs). This fusion enables an optimal balance between energy efficiency, security, and decentralization. A systematic design of the hybrid mechanism is provided, and then analytical performance comparison with standard PoW, PoS-only, and PoET-only models is presented. The results indicate that the hybrid model has significant energy consumption and latency decreases, making it a perfect candidate for implementation within resource-constrained environments such as agricultural and healthcare digital twin infrastructures. The paper ends by emphasizing the potential of the suggested consensus model to facilitate sustainable blockchain ecosystems.
In a metaverse ecosystem composed of various sub-metaverses, each offering unique functionalities and use cases, secure cross-domain communication becomes an essential requirement. Traditional authenticated key establishment (AKE) methods typically rely on centralized servers for identity verification, thus introducing single points of failure and significant latency. While some blockchain-based approaches mitigate these issues, they remain vulnerable to malicious key uploads. This paper proposes a blockchain-assisted identity (ID)-based hierarchical key management system and illustrates a cross-sub-metaverse AKE protocol with provable security to solve single points of failure and the risk of malicious key uploads. The hierarchical structure is designed to manage and categorize users’ identities. Moreover, smart contracts are used to pre-verify uploaded user identities and public keys on the blockchain, eliminating the need to fully trust identity issuers and preventing erroneous submissions. We implemented a prototype of our proposed blockchain-assisted cross-domain key management scheme, achieving an average execution time of approximately 0.1 seconds per user operation. We also deployed our contract on the Ethereum test network, incurring 1,802k gas for registration and 1,625k gas for key additions/updates. Furthermore, we formally prove the protocol’s security under the extended Canetti-Krawczyk (eCK) model, highlighting its suitability for next-generation metaverse ecosystems.
Shalini, Abhay Bhatia, Dr. Parag Jain, Dr. Lokesh Kumar
Exceptional connectivity across global networks has been driven by the expansion of Internet of Things devices, while significant weaknesses in security, scalability, and data management have emerged. Distributed ledger technology offers creative solutions to these fundamental limitations. This article reviews the blending of Blockchain technology with IoT, analyzing its potential, challenges and current advances. The article also highlights various applications and future research directions. This review aims to provide a comprehensive understanding by synthesizing existing knowledge, identifying research gaps, and establishing the context for future studies of blockchain-IoT integration, emphasizing critical design considerations and practical implementations.
The increasing deployment of Agricultural Internet of Things (Ag-IoT) systems is transforming food production and enabling climate-resilient farming practices.However, the growing reliance on interconnected sensing, automation, and cloud platforms significantly expands the attack surface, exposing agricultural operations to cyber threats that can disrupt critical processes, compromise data integrity, and undermine food security.This paper explores adaptive cybersecurity mechanisms designed to enhance the resilience of Ag-IoT ecosystems operating under climate-induced environmental and network constraints.The proposed approach integrates context-aware access control, federated threat learning, zero-trust architectures, and distributed ledger technologies to secure dataflows, device interactions, and supply-chain processes.Experimental evaluations and simulated farm scenarios demonstrate improved attack detection, operational continuity, and system reliability during extreme weather events and adversarial conditions.The results suggest that adaptive cybersecurity strategies are essential for protecting next-generation digital agriculture and ensuring resilient, secure, and sustainable food systems in an era of accelerating climate variability.
Eman Alatawi, Anoud Alhawiti, Doaa Albalawi, Umar Albalawi
The rapid expansion of the Internet of Things (IoT) has led to billions of interconnected devices generating and exchanging sensitive data across diverse domains, which introduces challenges in identity management (IdM) regarding privacy, scalability, and verifiability. While blockchain technology provides decentralization and tamper resistance, its transparency and increasing on-chain storage demands make it unsuitable for large-scale IoT identity ecosystems. To overcome these challenges, IoT-SBIdM is proposed as a lightweight, privacy-preserving, and stateless blockchain-based identity management framework designed for IoT environments. This framework incorporates Elliptic Curve Cryptography (ECC)-based accumulators and Zero-Knowledge Proofs (ZKPs) to facilitate selective disclosure, enabling entities to prove credential authenticity without exposing sensitive identity information. Furthermore, the framework adopts W3C-compliant Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to promote interoperability and user-controlled identity ownership. The experimental results indicate that IoT-SBIdM achieves efficient smart contract execution by reducing gas costs through optimized registry logic. Moreover, the system maintains a compact block size of only 45 MB at higher block heights, outperforming comparable schemes in storage efficiency by achieving a 55% reduction relative to recent models and an approximate 94% reduction relative to older systems, thereby demonstrating superior scalability and storage efficiency, making it suitable for identity management solutions for IoT environments.
The rapid expansion of the Internet of Things (IoT) has led to unprecedented growth in real-time data generation, and this growth is raising issues concerning data security, privacy, and scalability. Conventional data processing mechanisms based on centralization are confronted with high latency, single points of failure, and vulnerability to cyberattacks. To address such issues, in this study, blockchain and Federated Learning (FL) are being used to implement an end-to-end secure and decentralized system for IoT data analytics in real time. Federated Learning enables IoT devices such as wearable health sensors, industrial sensors, and home automation devices to locally train AI models and transfer model updates rather than raw data and thereby ensure privacy and avoid communication overhead. To ensure security and trustfulness in model updates, a blockchain network using Hyperledger Fabric and Quorum is integrated with FL to avoid tampering and keep the process transparent with a decentralized ledger. Smart contracts are employed to authenticate and aggregate model updates so that only trusted devices can participate in training. Recurrent Neural Networks (RNN), Reinforcement Learning (RL), and Random Forest (RF) models are employed to enhance learning efficiency in the research. A database of 3,450 records is obtained from IoT sensors, and performance is quantified in terms of accuracy in the models, blockchain transaction speed, latency, energy usage, and bandwidth efficiency. The findings show that RNN is 97.86%, RL 93.4%, and RF 90.23%, confirming the success of the proposed system. The research highlights the potential of blockchain-FL integration for privacy-preserving training of AI in large-scale IoT applications, making it relevant to application areas such as healthcare, finance, and industrial automation.