The rapid expansion of IoT in smart hospitals enables continuous patient monitoring, automated diagnosis and real-time clinical decision support. However, centralized healthcare data systems remain vulnerable to unauthorized data modification, single points of failure and poor audit transparency threatening patient safety and regulatory compliance. This paper proposes a Block-chain-IoT (BC-IoT) framework built on a three-tier hierarchical architecture. The first tier connects heterogeneous medical devices (ECG monitors, glucose sensors, infusion pumps, pulse oximeters) through a lightweight IoT sensor layer. The second tier applies AI-based anomaly detection at edge computing nodes. The third tier employs a dual-block-chain approach, combining the IOTA Tangle protocol for feeless micro-transactions with a permissioned Hyperledger Fabric network for enterprise-grade data governance. Medical data is encrypted using AES-256 and TLS 1.3, screened for anomalies at edge nodes and stored on an immutable distributed ledger. Smart contracts enforce role-based access control, ensuring only authorized personnel can access or modify patient records. The Inter Planetary File System (IPFS) handles decentralized storage of large medical files, with cryptographic content identifiers stored on-chain for full traceability. Evaluation on a simulated smart hospital testbed with 350 IoT nodes across five department’s demonstrated strong results: 42ms transaction latency, 1,250 transactions per second, a 99.7% tamper detection rate and a 99.98% data integrity score outperforming existing block-chain-IoT healthcare systems across all key metrics. The BC-IoT framework offers a scalable, energy-efficient and standards-compliant solution for securing digital health infrastructure.
Today, many computing workloads are executed in loosely coupled, geographically distributed environments where resources are owned by different organizations. Examples include inter-institutional research infrastructures, community-operated clusters, and edge deployments. As disconnections are frequent in such environments, ensuring reliable task execution remains a fundamental challenge. Kubernetes, the de facto standard for cluster orchestration, provides centralized control and strong consistency, but suffers from slow recovery when node failures occur frequently. At the opposite extreme, blockchain-based orchestration removes centralized control but incurs substantial latency due to global consensus, making it unsuitable for time-sensitive task scheduling. This paper presents Mutual Cloud, a decentralized orchestration framework that operates between these two extremes. Mutual Cloud adopts a hybrid architecture where task admission and queue management are handled in a centralized manner similar to conventional public clouds, whereas most scheduling functions, including execution-node selection and failure handling, are performed in a decentralized manner by autonomous agents using a distributed hash table. We implement a prototype of Mutual Cloud and evaluate its performance through large-scale simulation studies. The results show that Mutual Cloud maintains stable performance comparable to centralized baselines under normal conditions while achieving approximately five-second-level recovery latency under substantial node failures.
The current methods don't meet the security and performance needs of Internet of Vehicles (IoV) apps, and they also don't give the end user a low-latency, secure edge-computing service at the same time, while in the context of vehicles. This study presents a blockchain-enabled edge computing architecture that employs Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) for consensus, aiming to simultaneously enhance latency, energy efficiency, and security. The containerised architecture uses Hyperledger Fabric with Kubernetes to efficiently manage micro-services and move tasks off of them. In urban, suburban, and highway settings, the framework consistently outperforms baseline algorithms, with a 30–45% improvement in end-to-end latency and a 55% reduction in energy use under moderate to heavy loads. The system finished more than 95% of its tasks while keeping block consensus times under 1.2 seconds at peak loads. The architecture also showed consistent performance with different levels of vehicle density and used zero-knowledge proofs with attribute-based security to protect data from cyber threats from bad actors. These findings indicate that the integration of DDQN and blockchain will mitigate security issues in the Internet of Vehicles (IoV) by enabling secure edge computing for future vehicular networks.
The fast move toward sixth-generation (6G) distributed networks is making it possible to create highly dynamic, intelligent, and collaborative service environments for a wide range of use cases, including smart cities, autonomous systems, industrial IoT, immersive communication, and edge intelligence. But working together on a large scale in 6G environments comes with a lot of technical problems, such as the need for instant access to resources, coordinating different types of services, exchanging data that can grow, and making sure that security, trustworthiness, and privacy are all strong. Traditional centralised architectures have trouble meeting these needs because they have single points of failure, limited transparency, and problems with managing trust. Blockchain technology provides decentralisation, immutability, and the establishment of trust; however, its fundamental limitations in throughput and storage capacity impede its direct implementation in extensive 6G distributed collaboration systems. This paper presents a universal blockchain-based collaboration architecture specifically designed for 6G distributed networks, accompanied by an end-to-end collaboration mechanism aimed at delivering efficient, secure, and reliable resource-sharing functionalities. The proposed architecture combines service-oriented design ideas with adaptive blockchain improvements to get around problems with scalability. To address the throughput constraints of traditional blockchain systems, a service-oriented, capacity-adaptive blockchain sharding framework is proposed. In this framework, network nodes with different levels of consensus efficiency are dynamically split into different shards using a strategy that rates nodes based on their reputation. The assessment checks the performance of nodes by looking at things like their computational power, communication delay, reliability, and past behaviour. This is to make sure that shard formation is fair and reliable. Also, transactions are grouped by service type and sent to the right shards, which have the right level of consensus for the service. This service-aware transaction assignment makes sure that high-performance shards handle services that need to be processed quickly, while shards with moderate consensus capabilities handle services that don't need to be processed as quickly. This kind of adaptive alignment between service characteristics and shard performance greatly improves the overall throughput of the system and the efficiency of resource use. To make consensus even more efficient when workloads change, a load-sensitive Practical Byzantine Fault Tolerance (PBFT) mechanism is suggested for intra-shard consensus. The proposed load-aware enhancement dynamically changes consensus parameters based on shard load conditions, which is different from regular PBFT, which may slow down when there are a lot of transactions. This adaptive approach cuts down on communication overhead, makes the system more fault-tolerant, and keeps consensus performance stable even when many people are working together. So, the architecture makes sure that transactions are always valid while still meeting the ultra-low latency and high reliability needs of 6G apps. Along with throughput issues, storage scalability is still a big problem for blockchain-based systems because the ledger size keeps getting bigger. The paper proposes a hybrid storage policy that combines both on-chain and off-chain storage methods to get around this problem. To keep things immutable and trustworthy, important metadata, transaction proofs, and security-related records are kept on-chain. Large amounts of service data and information about sharing resources are kept off-chain using distributed storage solutions. Secure cryptographic connections between on-chain and off-chain parts make sure that data is accurate and can be verified without putting too much strain on the blockchain ledger. This mixed strategy greatly reduces the pressure on storage while keeping things clear and traceable. A lot of simulations are done to see if the proposed architecture and mechanisms are possible, can be scaled up, and will work better than other options. The results show that this new way of working together on a blockchain has a lot better throughput, less consensus latency, more balanced shard usage, and better storage efficiency than traditional blockchain-based collaboration models. Also, the proposed framework offers strong security guarantees and is resistant to bad behaviour in networks with different types of devices. In general, the suggested universal blockchain-based collaboration architecture is a scalable, secure, and adaptable way to make resource sharing in 6G distributed networks more efficient. The framework effectively solves blockchain scalability problems while also meeting the strict performance needs of next-generation distributed communication systems by combining service-aware sharding, load-sensitive consensus optimisation, and hybrid storage design. The results show that the architecture has the potential to be a key part of trustworthy and smart collaboration in future 6G ecosystems.
Federated unlearning enables clients to withdraw their contributions from a global model.However, enabling clients to verify whether the server has honestly and effectively removed their contributions remains a critical challenge. To address this aspect, which has been largely overlooked in existing literature, a verification model based on zero-knowledge proofs was constructed, and a comprehensive framework for verifiable federated unlearning was proposed. Combined with a dynamically updated Merkle tree structure, a novel verifiable federated unlearning scheme was presented characterized by its zero-knowledge property. This allows for the efficient generation of cryptographic proofs for server unlearning operations while rigorously protecting the data privacy of other clients. We evaluate the effectiveness and computational overhead of the proposed scheme. Comparative experiments with Rivest-Shamir-Adleman (RSA) accumulator-based and Hash chain-based schemes demonstrate that, when the model parameter size reaches the order of <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><msup><mrow><mn mathvariant="normal">10</mn></mrow><mrow><mn mathvariant="normal">5</mn></mrow></msup></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/B6D6E598-14B1-468e-9A32-73199F9CD69E-M002.jpg"><?fx-imagestate width="4.23333359" height="2.53999996"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/B6D6E598-14B1-468e-9A32-73199F9CD69E-M002c.jpg"><?fx-imagestate width="4.23333359" height="2.53999996"?></graphic></alternatives></inline-formula>, the proposed scheme improves proof generation speed by approximately two orders of magnitude and verification speed by 13.2 times compared to the RSA-based scheme. Furthermore, it effectively avoids the scalability bottleneck of data linear growth in verification overhead inherent in Hash chain-based schemes.
The proliferation of Internet of Things (IoT) devices creates unprecedented security, privacy, and transparency challenges in distributed systems. Traditional encryption-based approaches provide privacy but impose significant computational overhead, storage bloat, and key management complexity. This paper presents CIPHER-IoT, a blockchain-based framework that integrates Zero-Knowledge Proofs (ZKPs) with Hyperledger Fabric for privacy-preserving IoT data management. Unlike encryption-based approaches that store encrypted data on-chain, CIPHER-IoT utilises Groth16 zk-SNARKs to generate cryptographic proofs of data validity while storing only commitments on the blockchain, achieving stronger privacy guarantees with lower storage overhead. The framework employs Ed25519 for lightweight digital signatures and implements comprehensive chaincode for ZKP verification, commitment uniqueness checking, and access control enforcement. CIPHER-IoT targets gateway/edge IoT deployments with moderate computational capacity (ARM processors 500 MHz+) rather than ultra-constrained sensors. We evaluate CIPHER-IoT against two baseline systems, SPAS (homomorphic encryption-based) and SPAS-H (AES encryption with Hyperledger Fabric), using realistic simulation with 50–500 devices and transaction rates of 10–75 TPS. Experimental results demonstrate superior privacy (98% confidentiality vs. 80–95% for encryption-based approaches) alongside competitive performance: read latency improves 37% (p < 0.001), throughput increases 14.6% (p < 0.001), memory reduces 21.4%, network bandwidth saves 47%, and disk I/O reduces 37.8%. The system maintains zero data loss under failure scenarios and scales linearly to 500 devices with minimal degradation (9.9%). CIPHER-IoT demonstrates that verification-based privacy mechanisms can achieve stronger privacy and better performance than transformation-based approaches in distributed validation contexts, particularly suitable for enterprise IoT deployments requiring coordinated privacy-preserving infrastructure.
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.
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.
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.
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.
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.
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.
SATHISHKUMAR RANGANATHAN, Muralindran Mariappan, M. Karthigayan
Swarm robotics is an emerging field capable of accomplishing complex tasks through collective behaviour. However, it continues to face persistent challenges in secure communication, decentralized decision-making, and scalability. To operate effectively in resource-constrained environments, swarm networks require a decentralized mechanism that is secure, fast, and efficient. Although many studies have explored the use of blockchain technology for swarm robotics, existing blockchain consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and their variants remain unsuitable due to high computational complexity and risk of stake centralization. To address these challenges, we introduce the blockchain-based Rotational Leadership Role (RLR) consensus algorithm, a voting-based consensus re-engineered from the Raft approach, together with Decentralized Task Authorization and Validation (DeTAV), a token-based mechanism for context-aware task validation. This design ensures efficiency, security, and scalability in swarm robotics and drone systems. RLR is lightweight and well suited to operate within the limited computing resources of small robots or aerial drones. To validate its performance, a custom-built robotic simulator was developed as part of this research. Experiments conducted with up to 70 concurrent robots demonstrated that RLR consumed under 90 MB Random Access Memory (RAM) and 12% Central Processing Unit (CPU), whereas PoW required 460 MB RAM and 27% CPU with a minimum difficulty level of 21, reflecting an 80% reduction in memory usage and a 55% reduction in CPU consumption. Scalability tests with 4 to 70 robots further revealed RLR’s scalability with an average of 78% higher throughput, 47% lower election latency, and 34% lower consensus latency. Additionally, under the simulated attack scenarios and assuming uncompromised cryptographic keys, DeTAV’s context-based validation consistently achieved 100% success in detecting and isolating Byzantine nodes, while reducing Quality of Detection (QoD) time by 67%. Collectively, these results confirm that RLR with DeTAV effectively meets the efficiency, security, and scalability requirements of swarm robotic and drone networks.
This paper introduces DMind-3, a sovereign Edge-Local-Cloud intelligence stack designed to secure irreversible financial execution in Web3 environments against adversarial risks and strict latency constraints. While existing cloud-centric assistants compromise privacy and fail under network congestion, and purely local solutions lack global ecosystem context, DMind-3 resolves these tensions by decomposing capability into three cooperating layers: a deterministic signing-time intent firewall at the edge, a private high-fidelity reasoning engine on user hardware, and a policy-governed global context synthesizer in the cloud. We propose policy-driven selective offloading to route computation based on privacy sensitivity and uncertainty, supported by two novel training objectives: Hierarchical Predictive Synthesis (HPS) for fusing time-varying macro signals, and Contrastive Chain-of-Correction Supervised Fine-Tuning (C$^3$-SFT) to enhance local verification reliability. Extensive evaluations demonstrate that DMind-3 achieves a 93.7% multi-turn success rate in protocol-constrained tasks and superior domain reasoning compared to general-purpose baselines, providing a scalable framework where safety is bound to the edge execution primitive while maintaining sovereignty over sensitive user intent.
We introduce FlashChain, a decentralized framework that integrates IO-aware attention mechanisms—especially FlashAttention—into scalable, trustless AI systems. As Transformer-based models become foundational to Web3 infrastructure (e.g., DAOs, decentralized search, autonomous agents), their quadratic compute and memory bottlenecks present critical challenges. FlashChain adapts block-sparse FlashAttention into a modular architecture optimized for multi-node, low-bandwidth environments typical of blockchain and edge networks. We propose a hybrid protocol combining attention kernel optimization with zero-knowledge verifiability, enabling real-time, trustless AI inference across distributed nodes. Benchmarks show 3–5× speedups and up to 30× gas savings per inference compared to baseline on-chain models.
This research presents the design and implementation of the Decentralized Smart City of Things (DSCoT), a novel framework leveraging Web3 architecture to enhance the security and authentication of assets in cyber-physical systems (CPSs) for smart cities on a private blockchain. Unlike traditional non-fungible tokens that primarily identify and distinguish financial assets, existing approaches lack robust mechanisms for attributing and authenticating CPS assets such as owners, users, and IoT-enabled smart devices. DSCoT addresses this gap by introducing an extended ERC721 protocol, enabling IoT-enabled devices to have unique blockchain identities similar to user accounts, which enhances device management and tracking. Novel smart contract modules facilitate secure identification and authentication of CPS assets. Evaluated results on a private Hyperledger Besu blockchain show that DSCoT achieves significant performance improvements, including sub-second latency (∼0.1–0.5 s for application programming interface (API) calls), minimal transaction costs (∼0.01–0.05 USD), and a high processing capacity (∼1000 transactions per second (TPS)). These results, along with improved security through immutable authentication records, demonstrate DSCoT’s effectiveness as a scalable and secure solution for smart city CPSs.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Syed Raza Abbas, Zeeshan Abbas, Mobeen Ur Rehman, Seung Won Lee
Background Blockchain is increasingly explored as an infrastructure to mitigate data fragmentation, security incidents, and limited patient control in digital health ecosystems. This systematic review analyzed applications of blockchain in smart health systems, with a focus on security models, interoperability approaches, and integration with Internet of Things (IoT) and artificial intelligence (AI). Methods Following PRISMA 2020, PubMed, IEEE Xplore, ScienceDirect, Springer, and Google Scholar were searched for studies published between January 2019 and August 2025 using a predefined strategy combining the terms (“blockchain” OR “distributed ledger”) AND (“healthcare” OR “medical” OR “health records”) AND (“security” OR “privacy” OR “interoperability”); of the 1847 records screened, 26 studies met the eligibility criteria. Results Across these studies, blockchain most consistently strengthened electronic health record management by providing cryptographic access control, tamper-evident and immutable audit trails, and support for cross-institutional data exchange. In four multi-institutional settings, coupling blockchain with AI enabled privacy-preserving federated learning for collaborative diagnostics without centralized data pooling. However, several technical and regulatory constraints were reported, including limited scalability (median throughput <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mo>≈</mml:mo> </mml:math> 850 transactions/second vs. >10,000/seconds typically required for national infrastructures), high energy consumption in proof-of-work based schemes, and unresolved tension between immutable ledger storage and data protection rules such as the General Data Protection Regulation “right to be forgotten.” Conclusion Overall, the evidence indicates that blockchain is a credible enabler of secure, interoperable, and patient-governed health data sharing, provided that future deployments incorporate Layer-2 or comparable scalability mechanisms, adopt energy-efficient consensus protocols, and operate within clearer regulatory guidance on the permanence of clinical data.
Mnemosyne: Post-Quantum Distributed AI Infrastructure via Physical Security Barriers, Speculative Consensus, and Proof-of-Useful-Work on Heterogeneous Edge Networks Overview Mnemosyne is a theoretical framework and system design for running large language model (LLM) inference on heterogeneous edge devices — from Raspberry Pi to high-end workstations — with privacy guarantees that remain valid even after quantum computers break all existing cryptographic assumptions. This paper presents 14 original theorems and 3 new network protocols, spanning five interconnected layers: Layer 1 — OS-Level Memory Management (Ch. 3.1)Formalizes a 6-tuple system model covering semantic-aware LRU page replacement, zero-copy mmap, and delta encoding. Defines four system invariants verified via TLA+ specification. Layer 2 — Information-Theoretic Compression (Ch. 3.2–3.4, Theorems 5.1–5.3)Proves that delta encoding of LLM embedding sequences achieves a lower differential entropy bound when adjacent vector correlation ρ > 0.5. Static analysis of LLaMA-2-7B confirms ρ ≈ 0.85, yielding a theoretical compression gain of ~10.88× over FP16. Full invertibility and floating-point stability bounds are proven. Layer 3 — Thermodynamic Privacy Guarantee (Ch. 5–6, Theorems 7.1–8.4)The core contribution of this paper. Mnemosyne's privacy guarantee is grounded in Landauer's Principle and the Second Law of Thermodynamics, not computational hardness assumptions. Theorem 8.3 proves that exhaustive reconstruction of compressed embeddings requires a minimum energy of 10^{38,778} joules — approximately 10^{38,709}× the total energy of the observable universe. This makes Mnemosyne the first federated learning system, to our knowledge, whose privacy bound is elevated to the level of a physical law. The system is formally characterized as an Inverse Maxwell's Demon: it actively amplifies entropy to make information reconstruction thermodynamically infeasible, rather than computationally difficult. Layer 4 — Distributed Consensus (Ch. 7, Theorems 9.1–9.2)Proves the existence and feasibility of a Global Decentralized Compute Grid (GDCG) across heterogeneous hardware. Introduces a Byzantine Fault-Tolerant (BFT) extension of the MESI protocol with three new states (RS, PF, EC), enabling zero-copy memory sharing across devices. Theorem 9.2 proves that the system-recognized Modified state exists in at most one node among all nodes (including Byzantine nodes) at any time. Layer 5 — Economic Incentive Model (Ch. 7.4, Protocol 2)Defines Proof-of-Useful-Work (PoUW), a five-dimensional incentive function replacing wasteful Proof-of-Work mining with verifiable AI inference contributions. Projected annual reward: USD 100–500 per edge device. Key Contributions First federated learning system with privacy guarantee grounded in the Second Law of Thermodynamics 14 original theorems spanning information theory, thermodynamics, distributed systems, and formal verification 3 new network protocols (BFT-MESI extension, PoUW, QClock consensus) Formal verification via TLA+ and Z3 SMT Solver Minimum hardware requirement: 8 GB RAM (ARM Cortex-A76 class), enabling LLaMA-2-7B inference on commodity edge devices Keywords Edge AI · LLM Inference · Landauer's Principle · Post-Quantum Security · Delta Encoding · Product Quantization · Byzantine Fault Tolerance · Distributed Systems · Information Thermodynamics · Maxwell's Demon · Proof-of-Useful-Work · Federated Learning
Abdullah Ayub Khan, Abdullah M. Baqasah, Majed Alsafyani, Hamed Alsufyani · 6 authors
The revolution in Blockchain Distributed Ledger Technology (BDLT) is changing conventional structures and creating previously unattainable opportunities across a variety of industrial fields. This study explores new developments, opportunities, and trends while tackling important issues that highlight the revolutionary potential of BDLT. For secure, automated, and dependable ecosystem management, it focuses on innovations like Denaturalized Finance (Defi), chaincode, and BDLT interface with the Internet of Things (IoT). The investigation of hybrid blockchain models, which combine the benefits of private and public blockchains, is a novel component of this research. It provides a customized strategy to guarantee improved scalability, privacy, and performance. Conversely, this study highlighted the critical function of Hyperledger, a modular framework that makes enterprise-level blockchain solutions possible. Thus, Ethereum is a flexible platform with strong chaincode capabilities that facilitate the creation of Distributed Applications (DApps). Such opportunities for advancements are evaluated closely in order to demonstrate how they contribute to practical uses and innovations unique to a given sector. To improve worldwide acceptance, the paper also presents Systematic Literature Review (SLR) in order to demonstrate the existing innovative frameworks, especially Hyperledger Technology (HT) for resolving constraints such as consensus protocols for energy efficiency and adaptive regulatory models. For technological experts, industrial developers, and third-party policymakers seeking to harness BDLT's disruptive capabilities while navigating its complexity, this paper offers new viewpoints and practical insights to help close the gap between theoretical innovation and real-world applications.