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.
The proliferation of Web3 and Internet of Things (IoT) applications generates unprecedented volumes of real-time data streams, demanding secure and efficient subscription mechanisms that uphold data sovereignty. While decentralized architectures are the logical paradigm to ensure this sovereignty, a prominent class of existing schemes suffers from critical vulnerabilities—notably revocation attacks and prohibitive communication overhead—that severely hinder their practical deployment in large-scale environments. This paper introduces SegSub, a novel decentralized data subscription scheme specifically designed to significantly enhance both security and efficiency. SegSub's core innovations include the Segmented Dual-Key Regression with Binary Hash Trees (SDKR-BHT) mechanism, which partitions key regression chains into isolated segments to effectively contain potential data leakage and optimize token management and a strategic user grouping policy that localizes key updates, thereby substantially reducing system-wide communication overhead during revocation events. We formally quantify security improvements using a proposed security index and demonstrate a configurable trade-off between security and efficiency. Theoretical analysis and extensive experimental results validate that SegSub's security index is inversely proportional to segment length while communication efficiency is directly proportional. Furthermore, our grouping policy significantly reduces communication costs in large-scale scenarios through optimal group sizing. SegSub offers a robust and adaptable foundation for sovereignty-preserving data subscription services in Web3, empowering system designers with precise control over the critical security-efficiency balance to meet diverse deployment requirements.
Traditional Internet of Things (IoT) architectures suffer from centralization-induced vulnerabilities like single points of failure and data tampering. This paper proposes BITS, a lightweight blockchain-based framework designed for resource-constrained IoT environments. Its core is a novel Reputation-based Proof of Stake (RPoS) consensus algorithm that combines staking with behavioral reputation to elect reliable validators, significantly reducing energy consumption and computational overhead. BITS also employs smart contracts for automated device authentication and access control, alongside lightweight security techniques inspired by Advanced Metering Infrastructure (AMI). Theoretical and simulation-based analyses demonstrate that BITS achieves a throughput and latency profile compatible with typical IoT application requirements, while exhibiting superior resilience against DDoS attacks and malicious nodes compared to both Proof of Authority and centralized models. BITS effectively balances security, decentralization, and efficiency, offering a viable solution for enhancing trust in IoT ecosystems.
Hope Leticia Nakayiza, Love Allen Chijioke Ahakonye, Dong‐Seong Kim, Jae‐Min Lee
In cyber-physical multi-agent systems, ensuring secure and decentralized cooperation among autonomous agents is crucial for maintaining system integrity and operational reliability. However, the presence of malicious or selfish agents in these systems often disrupts message delivery and trust. This paper presents a blockchain-based reputation management framework that integrates adaptive trust scoring, decentralized relay selection, and non-transferable soulbound tokens to incentivize cooperation and build reputation. By combining off-chain trust evaluation with on-chain transparency through the PureChain permissioned blockchain, the framework achieves secure, tamper-proof reputation management. Experimental results show average trust score above 80% under varying proportions of malicious behavior (20% to 80%). The proposed PureChain blockchain also achieved approximately 7.7 times lower transaction latency and 1.4 times higher throughput compared to Ethereum Sepolia, making it suitable for resilient communication in cyber-physical multi-agent systems.
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.
Mehdi Talaie, Farkhondeh Jabari, Asghar Akbari Foroud
Blockchain technology as a new technology has been able to attract the attention of global communities. The applications of blockchain technology are developing rapidly due to its very desirable features, including decentralization, high data security, visibility, transparency and programmability. Among the applications of blockchain technology, we can mention the optimization of water management systems and sustainable development of water. Therefore, this chapter first examines the concept and structure of blockchain and its function. It also examines the components of blockchain technologies, including consensus protocols, smart contracts, data encryption and distributed ledgers. Then, the history of blockchain technology is presented and the classification of blockchain-based systems is discussed. After that, the strengths, weaknesses, opportunities and threats of blockchain technology are evaluated. Some applications of this technology will be introduced. Also, considering the importance of water and its sustainability, the role of blockchain technology for the water industry and intelligent water management will be investigated.
Simon Fernandez-Vazquez, Omar Alexander León García, Julián Ramírez, Salvatore Cannella
Blockchain is currently a key focus in both academic and industrial domains. The pivotal next step for the sustainable growth of blockchain technology lies in its widespread adoption. This article delineates the research subjects, constraints, gaps, and emerging trends in blockchain within the hydrosystem sector. Our examination, based on 136 publications from the Web of Science (WoS), underscores a thorough exploration of issues, particularly emphasizing security, privacy, and latency. However, the suggested remedies for these issues are still in early stages of development. This study serves as a useful starting point for researchers, offering insights and recommendations for future research areas in the application of the distributed ledger within the hydrosystem sector.
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.
Keqiu Li, Changzhi Li, Yang Shi, Dengcheng Hu · 7 authors
Blockchain-based Federated Learning (BCFL) has attracted considerable attention in the intelligent IoT domain for its privacy-preserving and decentralized characteristics. Depending on their applicable scenarios, BCFL frameworks are categorized into two types: synchronous and asynchronous. However, synchronous BCFL struggles with low efficiency in heterogeneous IoT environments, while asynchronous BCFL suffers from slow convergence speed. In additional, Both BCFL incur significant resource consumption from blockchain consensus mechanisms which is unrelated to federated learning tasks, leading to resource wastage and poor scalability, making them unsuitable for large-scale IoT networks. To address these challenges, we propose CoCFL, a novel BCFL framework utilizing multi-chain collaboration. CoCFL introduces two lightweight sub-chains: PoCFL-CChain and PC-CChain, based on different FL strategy. PoCFL-CChain uses a synchronous FL strategy for learning devices with similar performance to generate high-accuracy models, while PC-CChain adopts an asynchronous strategy for heterogeneous devices, which can improving training efficiency. CoCFL assigns devices to suitable sub-chains based on their performance to carry out FL tasks and aggregates the sub-chain models into a global model. This multi-chain collaboration strategy enhances model accuracy and convergence speed and significantly improves the scalability of BCFL. In additional, the consensus mechanisms in CoCFL sub-chains not only maintain the blockchain ledger but also handle FL-related tasks such as detecting poisoning attacks, assigning roles, and distributing incentives. This design not only improving the efficiency of BCFL, but also enhances learning security and ensuring fair incentives. Experiments show that CoCFL improves learning accuracy by 6% and efficiency by 18% over existing BCFL frameworks. It also demonstrates excellent scalability, with time consumption liner decreasing as sub-chains increase, and can withstand up to 40% of poisoning attacks while ensuring fair incentives.
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.
This study presents a novel decentralized and secure Automatic Optical Inspection (AOI) framework utilizing blockchain technology and smart contracts deployed on Nvidia Jetson edge computing devices to address integration complexities, security vulnerabilities, and excessive energy consumption in traditional AOI systems. The proposed architecture leverages Jetson devices as both processing units for image analysis and blockchain nodes, creating a decentralized network where inspection results are recorded through smart contracts. This approach ensures data immutability, transparency, auditability, and system resilience through decentralization. Experimental evaluation across three Jetson device models (Nano, Xavier NX, and Orin Nano Super) demonstrate that blockchain operations consume minimal resources. With the newer hardware Jetson Orin Nano Super showing average CPU usage of only 5.35% during blockchain operations (including ordering service), significant computational capacity remains available for AI-driven image inspection tasks. This enables the simultaneous execution of computer vision and AI recognition, as well as secure blockchain data recording, on a single edge device. The research implementing blockchain technology in resource-constrained edge devices proves the feasibility of blockchain-secured AOI in manufacturing environments.
Physical Unclonable Functions (PUFs) and Hardware Security
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.
Amol Murgai, M. Vijay Bhasker Reddy, M. P. Vani, Prof. Supriya Jagtap · 6 authors
Cloud edge convergence is enabling real time personalized learning but come with security and trust issues. Centralized models cause single points of failure, and lack transparency. This paper proposes a framework for decentralized access control and auditability of blockchains and tamper resistance to try to make smart contracts work well. Prototype using Ethereum and IPFS: low latency in authorization, better discretion is achieved comparing with the centralized approaches. This paper presents a blockchain technology framework that will facilitate secure data sharing process in cloud-edge learning networks. The framework has smart contracts that enforce the dynamic policy of access to provide auditable records of all data transactions. A prototype implementation that is tested in a simulated federated learning scenario shows how the system can deal with access decisions with low latency and data integrity and audit beyond. These findings indicate that blockchain can be one of the possible ways of moving to decentralized, policybased information cooperation in the education sector, where information protection and institutional trust are critical.
ABSTRACT Objective Collaborative edge computing (CEC) addresses the service quality issues that arise from the limited resources of a single node in traditional edge computing architectures by integrating resources from multiple edge nodes. However, ensuring reliable task offloading in this collaborative environment remains a significant challenge. Existing solutions often struggle to balance the intelligence and trustworthiness of offloading decisions effectively. This imbalance can lead to poor performance and reduced task success rates, especially if tasks are offloaded to malicious nodes. Methods To tackle these challenges, this paper proposes a trust‐enabled decentralized task offloading scheme that combines blockchain technology and deep reinforcement learning (DRL). First, we introduce a blockchain‐based reputation mechanism within the CEC architecture to facilitate trusted collaboration among nodes, utilizing smart contracts for reputation management. Next, we propose a beta distribution‐based three‐factor reputation update (BTRU) algorithm to enhance the accuracy of reputation evaluation. Finally, we present a decentralized and trust‐enabled task offloading (DTTO) algorithm based on DRL, which uses on‐chain reputation data to guide agents in learning trustworthy task offloading policies, thereby maximizing offloading trustworthiness and task success rates. Result To thoroughly assess the effectiveness and practicality of our proposed scheme, we develop a testbed for CEC task offloading based on Kubernetes and Ethereum. Experimental results demonstrate that the BTRU algorithm effectively distinguishes malicious nodes, reducing their average reputation by 97.54%, with an improvement of 9.94% compared to competitive algorithms. Meanwhile, the DTTO algorithm significantly enhances the efficiency and reliability of task offloading, raising the task success rate by at least 3.04%, especially when the proportion of malicious nodes reaches 40%, its task success rate is at least 5.41% higher than that of competitive algorithms. Conclusion The proposed trust‐enabled decentralized task offloading scheme successfully combines blockchain‐based reputation management with DRL to achieve both intelligent and trustworthy task offloading in the CEC environments. The experimental validation confirms the scheme's effectiveness in identifying malicious nodes and improving task success rates under various system conditions.
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.