This paper presents TEOM (The Evolutionary Open Machine), a decentralized operating protocol designed for smart enterprise architecture. TEOM enables secure, scalable, and flexible integration across diverse organizational functions, from AI and IoT to finance and governance. By applying a combination of Hashgraph and blockchain technologies, TEOM provides a robust framework for decentralized service delivery, real time data sharing, and multi organization collaboration. It supports the creation of autonomous, fault tolerant ecosystems that scale effortlessly, ensuring high availability and fault resistance across distributed networks. TEOM’s extensibility allows seamless integration of legacy systems and third-party platforms, promoting interoperability within smart enterprise environments. The protocol also integrates advanced AI capabilities for intelligent decision making, predictive analytics, and federated learning. Through automated service provisioning and decentralized transaction management, TEOM fosters an agile, transparent, and adaptive framework for modern enterprise architecture, empowering organizations to operate efficiently in a zero-trust environment.
The Internet of Things (IoT) generates massive volumes of patient and healthcare data every day. Providing the required accuracy for data classification, processing time, and analyzing the vast volumes of data from IoT devices and sensors are said to be the main challenges in IoT. Cloud computing is widely utilized as the foundation for the technologies required to secure healthcare. The healthcare industry has the most promise for blockchain technology since it can be used to integrate fragmented systems, the standard of electronic medical records should be raised, and take a more patient-centric approach to healthcare systems. The objective is to protect medical data, enable patients to use it to support their medical care, and provide reliable consent protocols for data exchange between various institutions and apps. Provide a blockchain-based architecture that verifies user identity using the Secure Hash Algorithm (SHA256) and Proof of Stake (POS) cryptography consensus technique to guarantee EHR sharing across many electronic healthcare systems.In this study, we assessed the performance of our proposed architecture using several metrics, and found that blockchain is a reliable security solution for the upcoming IoT network.
Internet of Things (IoT) facilitates intelligent interconnection and data exchange between devices. However, existing data aggregation schemes face challenges such as edge node disconnections, high computational overhead, and limited fault tolerance, which affect the reliability and efficiency of the system. To address these issues, this paper proposes a smart contract assisted and fault-tolerant data aggregation scheme without a trusted authority, named Cap. To realize it, we integrate blockchain technology and homomorphic encryption. Through smart contracts, we dynamically select and switch edge nodes to ensure that the data aggregation process continues even if some nodes go offline. Moreover, by leveraging homomorphic secret sharing, we effectively minimize communication overhead and ensure that users can exit the aggregation process without jeopardizing data integrity. This scheme provides an efficient and reliable solution for secure data aggregation in Iot environments and enhances the robustness and data security of the system. Performance evaluation show that, compared with existing schemes, Cap reduces computational and communication overhead by 30%.
Pradeep Nazareth, Sathyaprakash T, Sharan S Shetty, Shrihith S Poojary
Internet of Things (IoT) devices are constrained by limited storage and processing capabilities, creating open entry points for cyber threats. These constraints limits the establishment of robust security using conventional, centralized methods. Blockchain technology presents a promising solution by offering a decentralized, secure, and tamper-proof method for storing data. This paper examines modern research directions for implementing blockchain to enhance the security and quality of IoT systems. The core strength of blockchain lies in its ability to protect information from corruption and unauthorized access through encrypted, distributed ledgers. However, a significant challenge remains where many standard blockchain implementations are computationally expensive and demand high processing power, making them unsuitable for lightweight IoT devices. Therefore, the primary issue is not the applicability of blockchain’s security principles to IoT, but rather the prohibitive cost and resource requirements for many practical use cases. This research focuses on overcoming these barriers to enable efficient, featherweight blockchain solutions for the IoT landscape.
The emergence of 6G-connected smart cities introduces unprecedented challenges in ensuring security, privacy, and scalability for billions of heterogeneous devices and mission-critical services. Traditional Zero Trust Architectures (ZTA) provide continuous verification but rely on centralized control, making them vulnerable to insider threats and single points of failure. Conversely, blockchain-based frameworks ensure immutability and decentralized trust but suffer from high latency and limited scalability. This paper proposes a novel Blockchain-Enabled Zero Trust Architecture (BZTA) that integrates blockchain’s distributed trust management with Zero Trust’s continuous authentication and micro-segmentation, optimized for 6G urban infrastructures.The contributions of this work are fourfold. First, we design a four-layer BZTA model incorporating decentralized identity management, Zero Trust Gateways (ZTGs), blockchain-based ledgers, and smart contracts for adaptive access control. Second, we formalize the methodological foundations of the framework, including trust computation, Zero-Knowledge Proof (ZKP)-based authentication, authorization logic, and Proof-of-Authority consensus mechanisms. Third, we present a comprehensive evaluation using analytical models and simulations. Results show that BZTA achieves 95% trust classification accuracy, sub-20 ms authentication latency compliant with 6G URLLC, revocation within 3 s, and throughput up to 50,000 transactions per second, while reducing authentication energy costs by 35–40% compared to blockchain-only systems. Fourth, we demonstrate that BZTA provides robust defense against spoofing, replay, insider, lateral, and location spoofing attacks, significantly outperforming both ZTA-only and blockchain-only approaches.The findings highlight BZTA as a scalable, resilient, and privacy-preserving security paradigm for 6G smart cities. By merging blockchain immutability with Zero Trust’s dynamic verification, BZTA enables secure and transparent deployment of critical urban services such as healthcare, transportation, and energy management. This work positions BZTA as a foundational step toward building resilient, citizen-centric, and quantum-ready smart city infrastructures.
K. Ramesh Babu, M. Ramesh, M Gnana Prasuna, G. Ganesh Kumar · 6 authors
This article presents the Adaptive Multi-Modal Federated Optimization (AMMFO) framework which was developed to tackle important challenges in data privacy, fairness, and accountability in learning in the context of federated learning. The AMMFO framework addresses the need to train models with data from multiple modalities available at the edge in a secure and efficient manner while building trust and respecting privacy in a decentralized system. AMMFO applies differential privacy to protect sensitive model updates from adversarial inference and it leverages a blockchain-based trust mechanism to allow for transparency, immutability and decentralized accountability. The framework optimally adapts learning across multiple data modalities enhancing communication efficiency and stability of model convergence. Results from experiments indicate that AMMFO achieves 8-10% higher accuracy compared to FedAvg and FedProx, and 7-12% greater privacy resistance compared to FedDP while exploring different privacy budgets. Additionally, AMMFO improves convergence time by 15–20 percent and achieves less than 8% blockchain overhead. Overall, these results demonstrate the AMMFO framework's balance of performance, privacy, and scalability which enables next generation AI systems that are situated within privacy and trust-worthy frameworks in domains such as healthcare, finance, autonomous systems, and smart cities.
In complex environments such as those incorporating distributed and edge computing, middleware plays a critical role in meeting the communication and performance requirements of distributed systems by providing communication flow and integration capabilities. Its inherent advantages, such as abstraction of complexities, enhanced interoperability and scalability, make it ideal for managing tasks such as federated learning in edge AI environments. In addition, by supporting secure and energy-efficient operations, the middleware fosters sustainability, enabling green blockchain solutions and low-power distributed ledger technologies (DLTs) to thrive for managing dynamic ecosystems such as dAIEDGE. This deliverable D5.3, "Middleware prototype" presents the first version of dAIEDGE middleware. This work has been developed during the first year of dAIEDGE project from M4 to M16. In general, the document outlines the first version of the middleware developed collaboratively with task partners, by the University of Salamanca (USAL) as part of Task T5.2, "Middleware and Networks for Edge AI," within the dAIEDGE project. This task reflects a joint effort involving multiple participants, including BCA, BTH, CETIC, KUL, VICOM, and UEDIN.
Electronic Health Records (EHR) is the main core of modern healthcare, but interoperability across different blockchain platforms is a key challenge. This work proposes a cross-chain middleware architecture, which facilitates secure and real-time synchronization of EHR data between Hyperledger Fabric (private blockchain) and Ethereum Sepolia Testnet (public blockchain). The framework integrates AES-256 encryption and Inter Planetary File System (IPFS) as decentralized storage to enhance patient privacy. To facilitate interoperability across the blockchains the research introduces a smart middleware layer. This layer autonomously monitors the blockchain events, processes encrypted CIDs, enforces real time cross chain consistency and smart contract-based access control. The experimental evaluation shows that proposed framework achieves low synchronization times (< 195 ms), low gas and latency costs, small encryption overhead (< 4–5 KB), robust file storage and retrieval through IPFS. Such positive evaluations with scalable and real-time deployment, sets the foundation of patient centric interoperable healthcare ecosystems.
This paper presents CommitFit, a blockchain-based fitness platform designed to enhance user engagement and data security through financial incentives and decentralized attestation protocols. The architecture integrates modular layers comprising a secure frontend, AI-driven backend verification, and Ethereum smart contracts for automated staking and non-fungible token (NFT) rewards. Empirical evaluation demonstrates significant improvements in user retention and goal completion rates compared to traditional systems, with notable accuracy in AI-based activity verification. The platform addresses critical challenges such as privacy, scalability, and integration with wearable devices. Comparative analysis with existing fitness solutions highlights CommitFit's advancements and future potential for broader applications in secure, decentralized health data management.
Arfa Mahvish, V Surekha, K Archana, Vaseem Ahmed Qureshi · 6 authors
The fast spurts of Internet of Things (IoT) devices have brought new challenges in security and privacy, such as data breaches, unauthorized access, and vulnerability of centralized systems. Traditional IoT security models are based on the centralized architectures and, therefore, have a lack of redundancy and a vulnerability to cyber-attacks. This paper introduces a secured decentralized IoT network based on blockchain technology to improve trust, integrity of data, and security in IoT ecosystem. The distributed ledger technology (DLT) of blockchain makes data transactions tamper-proof, access control transparent and based on decentralized authentication. By integrating smart contracts, the system automates secure device-to-device communication without intermediaries, reducing latency and improving efficiency. The proposed approach eliminates data silos, enhances network resilience, and mitigates security threats such as data manipulation, DDoS attacks, and unauthorized access. Performance analysis demonstrates that blockchain-based IoT security significantly improves data integrity, transaction transparency, and system reliability, making it an ideal solution for smart cities, healthcare, and industrial IoT applications.
Distributed Ledger Technologies (DLTs) underpin Digital Circular Economy (DCE) systems that rely on efficient IoT data flows. Shimmer, a DAG-based DLT optimized for IoT, enables feeless transactions with parallel validation through its tip-selection mechanism. On such ledgers, message fragmentation induces a latency–throughput tradeoff as per-block cost rises with parallel validation. Such efficiency lowers energy and congestion, supporting DCE objectives. Yet, end-users cannot control payload size or network load, leading to unpredictable latency and high CPU use on submitting devices, increasing energy consumption. Existing approaches mostly modify ledger internals, overlooking adaptivity or end-user policies. We introduce ABS-TD3, an offline-to-online TD3 agent that receives the total message size and outputs the optimal per-block size for balancing latency and energy-efficient CPU utilization. The agent is pre-trained offline on real data with Retrieval Augmentation and adaptive weights for improved decision making, then transitioned online with prioritized replay and a novelty bonus, balancing exploitation-exploration, yielding stable adaptivity compared to standard RL approaches. ABS-TD3 is implemented on Shimmer and can integrate with future Tangle-based forks of pre-IOTA-Rebased frameworks, exposing the same client-side controls. ABS-TD3 is evaluated on Shimmer by submitting 8 message sizes ranging from 5KB to 100KB, under the 32 KB block-size limit, with 250 iterations per size via IOTA-SDK. Against max, min, random, and fixed-weight baselines, it reduces median latency by about 9 % to 12 % and median CPU utilization by about 12 % to 17 % versus max and random policies, enabling efficient IoT data submission for DCE platforms without altering DLT infrastructure.
Securing data in the Industrial Internet of Things (IIoT) is critical due to the growing complexity and scale of industrial networks. Integrating Ethereum-based blockchain technology offers a promising solution by leveraging distributed ledgers to enhance the transparency, immutability, and security of IIoT systems. This study aims to enhance threat detection and data protection in IIoT environments through blockchain integration. To achieve this, the proposed approach incorporates Dynamic Threat Landscape (DTL)-based Intrusion Detection Systems (IDS) for real-time attack modelling, enabling systems to adapt to evolving threats. However, integrating blockchain with IIoT also presents challenges, including ensuring low latency for real-time processing, scalability to manage large volumes of sensor data, and maintaining robust cybersecurity while preserving data privacy and integrity. Addressing these concerns is essential for the effective deployment of blockchain-enabled threat detection in IIoT networks.
Short-shelf-life foods have received increasing attention in daily dietary practices due to their freshness and convenience. Compared with other food categories, they impose more stringent requirements on quality assurance and end-to-end traceability. To address the challenge of balancing timeliness in high-frequency real-time data collection with the authenticity of trusted information flow in short-shelf-life food supply chains, this paper proposes a trusted information collection mechanism based on lightweight blockchain technology. First, a multi-layer collaborative architecture is designed, encompassing the perception layer, edge gateway, blockchain layer, and application layer. By integrating off-chain storage with on-chain indexing, the mechanism effectively alleviates the storage and computational burden on the main blockchain. Second, the edge gateway layer incorporates a zero-knowledge interval proof module, enabling real-time, localized privacy compliance statements for sensitive data. Meanwhile, the blockchain layer adopts an PoA consensus mechanism, whereby authorized nodes perform rapid data verification and deposition. Finally, through theoretical analysis and simulation-based validation, the results demonstrate that the proposed mechanism not only enhances the real-time performance, authenticity, and privacy protection of data in short-shelf-life food supply chains, but also achieves high operational efficiency and scalability. This work thus provides a novel theoretical foundation and practical approach for trusted information collection in the context of short-shelf-life food supply chains.
Decentralized authentication in dynamic mobile networks faces significant challenges due to high node mobility, resource constraints, and vulnerabilities to side-channel attacks. In this work, we present MobiAuth , a blockchain-driven framework based on Hyperledger Iroha and OMNET ++ that enables secure, peer-to-peer authentication using compact Ed25519 signatures and ephemeral session keys. Our protocol eliminates single points of failure by distributing trust across a permissioned ledger and employs constant-time cryptographic operations to thwart timing and power-analysis attacks. We validate MobiAuth through co-simulation in OMNET ++ integrated with Iroha via a Python gRPC bridge and benchmark its performance with Hyperledger Caliper. Simulation yields 95% packet delivery with an authentication latency ranging from 12 ms in the only OMNeT ++ and baseline to 20–150ms in the full ledger-integrated system, and a ledger write throughput of 250tps. Comparative experiments demonstrate a 33% reduction in communication overhead and robust operation under random Control Point failures and Byzantine Access Node behavior. Analysis of on-device ledger synchronization further highlights practical storage growth and bandwidth requirements for long-term deployment. These results indicate that MobiAuth achieves strong security and privacy with modest energy impact, scalable performance, and compatibility with mobile devices in real-world network environments. • Vulnerabilities of mobile network devices in a dynamic environment. • Blockchain-based automatic authentication for mobile devices. • Enhanced security and privacy with Ed25519 curve cryptography. • OMNET++ simulation on Hyperledger Iroha for mobile network. • Protocol verification using Scyther for testing security protocol strength.
Jinwen Liang, Jiannong Cao, Bo Yang, Dongbin Bai · 6 authors
Decentralized Physical Infrastructure Networks (DePIN) represent a paradigm shift in infrastructure deployment and resource coordination, leveraging blockchain and token-based incentives to transform traditional service models. This paper presents a comprehensive survey of DePIN, encompassing its evolution, architecture, open issues, and practical implementation. We begin by tracing the development of DePIN across three key phases: the emergence of fungible tokens, the rise of non-fungible tokens (NFTs), and the shift toward real-world asset tokenization. We highlight representative DePIN projects and propose a unified four-layer architecture comprising the infrastructure, decentralized data, resource control, and application layers, each delivering essential functionality for scalable, interoperable DePIN systems. We then analyze open research challenges within each layer and outline promising future research directions. To demonstrate the viability of our architecture, we present DCEAI, a prototype DePIN platform that enables decentralized LLM inference via edge computing. We evaluate its performance and discuss opportunities for further enhancement. This survey aims to establish a solid foundation for future research and innovation in DePIN ecosystems.
Healthcare IoT systems must balance the need for continuous monitoring with strong guarantees of privacy and trust. We present ProofHealth, a zero-knowledge proof–based framework that shifts verification to the edge by generating zk-SNARKs on smartphones. In this design, wearable data is encrypted and accompanied by proofs that ensure only valid submissions are admitted to cloud storage, even on untrusted networks. We implement and evaluate ProofHealth under varying batch sizes, measuring latency, throughput, and proof size. Results show batching significantly improves per-sample efficiency while proof sizes remain constant at sub-kilobyte scale, enabling lightweight communication suitable for constrained devices. This demonstrates the practicality of proof-at-the-edge healthcare monitoring and establishes ProofHealth as a novel approach to secure and privacy-preserving health data collection.
The healthcare industry is increasingly dependent on digital technologies for the storage, access, and sharing of sensitive patient data. While this brings significant benefits in terms of efficiency and accessibility, it also raises critical concerns regarding privacy, security, and trust. This paper proposes a hybrid Quantum-Blockchain system for secure healthcare through a decentralized, tamper-proof method for managing Electronic Health Records (EHRs). We present the design and implementation of a decentralized application (DApp) that leverages Ethereum smart contracts and InterPlanetary File System (IPFS) to securely store and control access to patient records. The proposed system empowers patients with ownership and control over their medical data, while enabling authorized doctors to access records with explicit patient consent. The system shows a temporal performance efficiency for Ethereum-based DApp mining. However, the system remains approximately 19 times slower than traditional Proof-of-Stake protocols, indicating a trade-off between enhanced security and computational speed.
Behnam Khayer, Siamak Mirzaei, Hooman Alavizadeh, Ahmad Salehi Shahraki
Blockchain technologies offer transformative potential in terms of addressing the security, trust, and identity management issues that exist in large-scale Internet of Things (IoT) deployments. This narrative review provides a comprehensive survey of various studies, focusing on decentralized identity management, trust mechanisms, smart contracts, privacy preservation, and real-world IoT applications. According to the literature, blockchain-based solutions provide robust authentication through mechanisms such as Physical Unclonable Functions (PUFs), enhance transparency via smart contract-enabled reputation systems, and significantly mitigate vulnerabilities, including single points of failure and Sybil attacks. Smart contracts enable secure interactions by automating resource allocation, access control, and verification. Cryptographic tools, including zero-knowledge proofs (ZKPs), proxy re-encryption, and Merkle trees, further improve data privacy and device integrity. Despite these advantages, challenges persist in areas such as scalability, regulatory and compliance issues, privacy and security concerns, resource constraints, and interoperability. By reviewing the current state-of-the-art literature, this review emphasizes the importance of establishing standardized protocols, performance benchmarks, and robust regulatory frameworks to achieve scalable and secure blockchain-integrated IoT solutions, and provides emerging trends and future research directions for the integration of blockchain technology into the IoT ecosystem.
Denis Wapukha Walumbe, Gabriel Ndugu Kamau, Jane Wanjiru Njuki
Proof of Stake (PoS) models are energy-efficient and require limited computational power. These features are critical in telemedicine environments, where resource-constrained devices must handle sensitive data securely. The growing need for auditable and privacy-preserving data storage in telemedicine underscores the importance of PoS models optimized for lightweight devices while complying with strict regulatory requirements, such as the Health Insurance Portability and Accountability Act (HIPAA).This study was guided by two research questions: (i) Which PoS models are lightweight and suitable for telemedicine? and (ii) What features make lightweight PoS models effective for privacy and efficiency in telemedicine? To address these questions, a systematic literature review (SLR) guided by the PICOC framework was conducted to investigate lightweight PoS models that can enhance privacy in telemedicine systems. Out of 2,394 papers studies screened, 55 were included in the analysis. The findings identified Algorand, Ouroboros Praos, Tendermint, Nxt, and Casper CBC as promising candidates. Key enabling features included lightweight voting mechanisms, such as Byzantine Agreement protocols and Verifiable Random Functions, as well as cryptographic techniques like symmetric encryption and multiparty computation. Performance metrics evaluated included latency, throughput, energy efficiency, and battery consumption, with Grey Relational Analysis ranking Algorand highest due to its low latency, high throughput, and minimal energy consumption.
Ahmad A Alsharidah, Devki Nandan Jha, Ellis Solaiman, Bo Wei · 6 authors
Federated learning is a promising approach that enables collaborative machine learning (ML) in distributed environments, such as the Internet of Medical Things (IoMT) while preserving consumer privacy. It allows multiple consumers to collaboratively train a model using their own data, sharing only the locally trained model rather than the raw data. Most existing federated learning systems assume a high level of trust in participating nodes, which is unrealistic in real-world consumer-centric scenarios. Involving untrusted nodes can compromise the integrity of the training process and result in potential data breaches. To address these challenges, this paper presents REWARDCHAIN, a novel federated learning framework that leverages blockchain technology to ensure trust and accountability among untrusted IoMT consumers. By recording all model updates and client contributions on an immutable blockchain ledger, REWARDCHAIN allows auditing of the entire training process and attributing any malicious behaviour to specific nodes. Moreover, we design an incentive mechanism that evaluates contributions based on data quality and participant reputation. This system motivates participants to contribute high-quality data through a reputation-constrained reward allocation. Our evaluations show that REWARDCHAIN effectively balances trust, security, and model performance, facilitating a more secure and effective federated learning ecosystem.
A digital revolution is taking place in healthcare due to rising patient data volumes, concerns about privacy and security, and the need for interoperable solutions. The demand for trustworthy, scalable, and interoperable technologies has become essential in this fast-growing healthcare industry. Conventional centralised systems regularly encounter challenges meeting these requirements because of problems including storage structure, data privacy, security vulnerabilities, network complexity, and lack of scalability. Additionally, current blockchain implementations frequently face challenges with interoperability, trust management among several healthcare stakeholders, and throughput bottlenecks. To overcome these challenges, this paper introduces a novel framework called LIVER: a Lightweight Infrastructure for Verifiable Electronic Records that integrates Blockchain, InterPlanetary File System (IPFS), Edge computing, and Internet of Medical Things (IoMT) to address these issues. Distributed processing based on edge computing and IPFS, cryptographic methods, blockchain consensus mechanisms, and an immutable ledger allows it to be scalable, secure, interoperable, and boast tamper-proof data integrity. Furthermore, a unique queueing approach has been implemented to improve operational efficiency, which controls patient flow, decreases wait times, maximises resource utilisation, minimises healthcare delays, and enhances the patient experience. Finally, based on the experimental evaluation results, the LIVER framework reduces ledger size, facilitates enhanced data sharing, and improves the healthcare system’s efficiency, scalability, security, and performance. The goal is to provide a framework that can handle the computational and operational limitations of existing healthcare systems while still protecting patient privacy and allowing for scalability.