Adaora. A, Obayi, Caroline Asogwa, Blessing .C. Uzo
This study investigates traditional health care delivery systems to eliminate current inefficiencies by creating a decentralized appointment and referral management system using Web 3.0 technology, blockchain, smart contracts, and Decentralized Identity (DID) compatible with scalable cloud storage. In a series of multi-agent simulations run on the Ethereum and Polygon Testnets, the performance of the system under simulated high-load traffic scenarios was tested. The simulation results showed consistent transaction latencies (285 ms average), high throughput rates (34 appointments per second), and low errors rates (1.4%). Another innovation of this study was the development of a hybrid architecture that enables the storage of cryptographic hashes associated with medical records on-chain, while keeping patient data encrypted on off-chain servers. This allows the immutability and auditability of the data while still maintaining compliance with GDPR and HIPAA regulations by enabling patient data to be deleted from the system entirely. As a result, this system was significantly more secure, transparent, and operationally efficient compared to current centralized systems. These findings confirm and support the potential of decentralized technologies for Scalable, Trustworthy Medical Service Delivery of the Data.
Large-language-model (LLM) inference is increasingly concentrated in dedicated GPU data centres and closed API platforms, raising barriers for institutions that want to run, study, or contribute to AI infrastructure. We argue that democratizing inference requires an architecture in which smaller organizations can participate as operators, builders, and researchers rather than only as customers. We propose OpenPrism Network, an open, UMA-first distributed inference architecture in which transformer layers are statically owned by nodes so that weights remain resident and only activations transit the network; a blockchain layer is restricted to settlement, reputation, and payment and never to compute; output integrity is established by multi-node redundancy with tolerance-banded fingerprinting rather than zero-knowledge proofs; and routing is locality-aware, keeping inference within metro-area clusters. The network is explicitly scoped to batch- and throughput-oriented, latency-tolerant workloads. We describe two deployment models: a distributed mesh harvesting idle institutional capacity, and a purpose-built UMA micro data center deployable by resource-constrained organizations as a sovereign inference facility. We also describe an open participation model in which node operators, runtime implementers, benchmark maintainers, and application integrators can contribute through published interfaces and open-source reference components. This is a position and architecture paper: we claim no original experimental results, and all quantitative figures are drawn from publicly available benchmarks and published specifications, cited explicitly. We report performance per watt honestly, including the threefold cost of consensus, and find that UMA nodes lose on operational efficiency against batched data-centre GPUs in the scoped regime; the architecture's advantage is therefore established on capital in the harvested-capacity model, participation, and data sovereignty, while total cost of ownership for the purpose-built micro data center is mixed and strongly pricing-regime dependent, not universally favorable. We frame two problems as genuinely unsolved: a consensus protocol for ML output verification under floating-point non-determinism, and a dynamic layer-assignment protocol that rebalances ownership as nodes join and leave without full weight redistribution. We also state a concrete validation roadmap, including prototype scope, baselines, and evaluation metrics.
Dr.S.Janani, Ashwin Prabhu G., Hymlin Rose S. G., M. Kalaimani · 8 authors
Blockchain-enabled digital twin architectures are increasingly recognized as foundational to the development of smart hospital infrastructures that demand secure, interoperable, and intelligent healthcare systems. This chapter per the authors examines the conceptual and technical integration of blockchain technologies with digital twins to support trusted data exchange, real-time clinical modeling, and decentralized coordination across hospital ecosystems. The purpose is to articulate how distributed ledgers enhance data integrity, provenance, and access control, while digital twins enable continuous virtual representations of patients, medical devices, and hospital operations. The chapter synthesizes architectural frameworks, application scenarios, and system-level design principles, highlighting implications for clinical decision support, operational optimization, and regulatory compliance. Emerging challenges, including scalability, interoperability standards, and ethical governance, are also discussed to frame future research and deployment pathways.
S. Tamilselvi, Ravikumar R. N., Duggirala Aravind, Satheesh Kumar A. · 6 authors
The integration of Distributed Ledger Technologies (DLTs) with Digital Twin (DT) systems is transforming smart hospital infrastructures by facilitating secure, transparent, and real-time operational intelligence. As healthcare settings rely more on IoMT devices, AI analytics, and automated processes, it's crucial to ensure that the data shared between real and virtual systems is reliable and accurate. DLT provides decentralized validation, immutable storage, and automated smart-contract governance, ensuring trustworthy Digital Twin updates for predictive maintenance, patient-flow optimization, and resource management. By strengthening interoperability, enhancing cybersecurity resilience, and supporting transparent data-sharing mechanisms, DLT-enabled Digital Twins offer a robust foundation for next-generation intelligent hospital ecosystems. This chapter looks at different design models, real-life examples, performance details, and future research directions that are important for making healthcare changes that are scalable, secure, and ethical.
With the increasing adoption of mobile applications, data in the mobile cloud faces numerous security threats and privacy breaches. To overcome cyberattacks, ensuring confidentiality and data security for users’ sensitive data is pivotal in mobile cloud computing. Traditional security mechanisms involve data leakage during the verification process, while blockchain-dependent solutions lead to high resource consumption and latency. Additionally, collaborative data processing during data transactions can result in potential privacy attacks on users. This paper proposes a novel approach for maintaining a security framework for Microservice-based Mobile Cloud Computing (MSCMCC) using hybrid cryptographic frameworks such as Zero-Knowledge Proof (ZKP) and Secure Multi-Party Computation (SMPC). The proposed model validates users’ offloaded data using zk-SNARK and Groth16 for task verification and enables data analysis from multiple users without exposing raw data. SMPC is employed for privacy preservation during collaborative multi-party computation. Experimental results demonstrate that the proposed framework reduces power consumption, improves energy efficiency during processing by 30–35%, lowers computational costs, enhances security and privacy, and effectively manages dynamic load balancing compared to traditional cryptographic techniques.
Muruganantham Angamuthu, Safeyah Tawil, Gaurav Pushkarna, M. D. Boomija · 6 authors
Smart agriculture transforms food security, climate change, and resource optimization. In agriculture, IoT monitors soil, crop, irrigation, and supply chain operations in real time. As data-driven farming expands, stakeholders face security, transparency, trust, and interoperability issues. Centralized agricultural data management systems are unreliable due to tampering, illegal access, and single points of failure. Smart agriculture uses blockchain technology for secure crop tracking and data sharing via distributed ledger systems. The suggested architecture enables data immutability, traceability, and transparency across the agricultural lifecycle with IoT-enabled sensing and blockchain-based data storage and validation. Secure crop origin, cultivation, and logistics information is available to farmers, distributors, regulators, and consumers. Blockchain-supported smart agriculture enhances stakeholder trust, fraud reduction, and decision-making efficiency, studies show. The decentralized design allows resilient data exchange without centralization. We found that blockchain and IoT can modernize agricultural ecosystems, promote sustainable farming, and improve food supply chain integrity. This research develops secure, transparent, and intelligent digital agriculture systems.
Agricultural trade in developing countries continues to depend on informal agreements, multi-tier intermediary networks, and centralized payment mechanisms, resulting in payment delays of 30–90 days, information asymmetry, and weakened bargaining power for smallholder farmers. This paper presents AgriHandshake, a blockchain-based smart contract platform enabling direct crop trading between farmers and vendors through an automated escrow payment mechanism deployed on the Ethereum network. The system employs a hybrid architecture that stores cryptographic state hashes and escrow logic onchain while offloading trade metadata and delivery documentation to the InterPlanetary File System (IPFS), reducing average transaction gas costs to approximately 85,000–210,000 gas units per operation. A Solidity-based escrow contract enforces a structured four-state machine (CREATED→FUNDED→DELIVERED→COMPLETED/DISPUTED) with a 72-hour automatic payment-release timer implemented via block.timestamp. Experimental evaluation on the Ethereum Sepolia testnet demonstrates average smart contract function execution latency under 15 seconds, end-to-end trade confirmation within 3–8 minutes including IPFS upload, and strong resistance to reentrancy and front-running attacks. Comparative analysis against eNAM, FarMarket, and AgriOnBlock confirms that AgriHandshake is the first platform to combine a dedicated escrow payment guarantee, decentralized off-chain storage, and a farmer-centric usability model within a single deployable framework
Smart cities require the efficient and secure integration of key infrastructure domains such as water, energy, transportation, smart lighting and waste management deploying a myriad of data-generating IoT sensors and devices. Current management systems feature single points of failure, lack of auditability, insufficient privacy protection and lack of scalability as IoT nodes increase in density. In this paper, we present SmartChain, a three-tier multilayered blockchain based architecture that incorporates a permissioned distributed ledger, an AIbased anomaly detection module (ADM) and a dual-layered privacy preservation approach that combines Zero-Knowledge Proofs (ZKP) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE). SmartChain is tested on a large dataset - 2000 timestamped transactions involving Smart Cities’ five infrastructure types across several zones of a city. The results show a mean throughput of 3,421 transactions per second (TPS), a mean transaction latency of 3,847 milliseconds and a mean privacy score of 82.4 out of 100. The machine learning based anomaly module produces an F1-Score of over 97% and an AUROC score of 0.991 with Random Forest as the classifier. Benchmarking against Hyperledger Fabric 2.5, Ethereum 2.0 and the IOTA Tangle demonstrate the scalability, security, privacy and efficiency of SmartChain. This research renders SmartChain a practical production-level platform for management of new smart city infrastructure.
Sixth-generation (6G) networks are expected to provide ubiquitous connectivity, AI-native orchestration, and seamless integration across terrestrial and non-terrestrial infrastructures. However, these capabilities introduce new privacy challenges related to the classification and protection of personal, quasi-personal, and non-personal data in complex data-driven environments. This paper presents a systematic review of 78 peer-reviewed studies published between 2019 and 2025. Following a PRISMA-based methodology, this review analyzes privacy-enhancing technologies (PETs), regulatory compliance frameworks, and architectural patterns for privacy preservation in 6G networks. The findings show that differential privacy (DP) and federated learning (FL) dominate current research, accounting for nearly 52% of the reviewed studies. Blockchain auditing and zero-knowledge proofs (ZKPs) collectively represent approximately 30%, while the remaining mechanisms, including physical-layer security (PLS), trusted execution environments (TEEs), homomorphic encryption (HE), secure multi-party computation (SMPC), and anonymization, account for roughly 18%. These mechanisms exhibit varying levels of privacy strength, utility preservation, latency, and energy cost. At the same time, evolving regulatory frameworks, including GDPR, PDPL, CCPA/CPRA, LGPD, and PIPL, increasingly extend privacy obligations to quasi-personal and aggregated data. Building on these findings, this paper proposes a unified taxonomy that clarifies the boundary between personal and non-personal data. It also provides a cross-layer mapping between PETs and compliance requirements across the Core/SBA, RAN, Edge/MEC, and NTN layers. Finally, this paper presents a forward-looking roadmap for 2025–2030, highlighting hybrid PET pipelines, post-quantum auditability, and AI-driven compliance automation as key directions for privacy-preserving 6G standardization.
Introduction: The integration of blockchain technology with Internet of Things (IoT) devices in smart city deployments presents significant technical challenges, particularly regarding consensus mechanism design. Traditional blockchain consensus protocols such as Proof of Work (PoW) and standard Proof of Stake (PoS) impose computational, energy, and latency requirements that exceed the capabilities of resource-constrained IoT devices, motivating the development of lightweight alternatives. Materials and methods: This paper examines lightweight consensus mechanisms specifically designed for edge-optimised blockchain deployments, evaluates hybrid on-chain/off-chain architectural patterns, and presents a comparative analysis of emerging solutions. We propose four research hypotheses (H1–H4) regarding hierarchical consensus performance and validate them through extensive discrete-event simulation across five smart city domains, using a 500-node testbed calibrated to representative urban workloads. A novel Business Process Model and Notation (BPMN)-based process model formalises the three-tier consensus workflow, and security analysis quantifies Byzantine fault tolerance guarantees. Results: The proposed three-tier framework achieves a 94.7% ± 2.3% reduction in on-chain transactions while maintaining cryptographic auditability, with consensus latency under 500 ms for district-level operations (p < 0.001 vs. single-tier baseline). District-level throughput reaches 1247 ± 89 transactions per second (TPS), representing an 8.0× improvement over city-wide consensus, with energy consumption per transaction at the edge tier 95% lower than single-tier implementations. The simulation of 10,000 Byzantine attack attempts yielded a 99.97% detection rate. All improvements are statistically significant (p < 0.001, Cohen’s d > 0.8). Conclusions: Hierarchical consensus architectures, combined with selective off-chain processing, offer the most promising pathway towards scalable, secure blockchain–IoT integration in smart city contexts. Through an examination of five practical smart city use cases, we demonstrate that tailored consensus approaches can achieve the security guarantees necessary for critical urban infrastructure while respecting the computational limitations of deployed sensor networks. Remaining challenges in dynamic validator management, cross-chain interoperability, quantum resistance, and regulatory compliance are identified as priorities for future work.
Adil Raja, Ragini Kushwaha, Muhidinov Ayubbek Nuritdinovich, Girish H · 6 authors
The fast-growing Internet of Things has opened up new possibilities of automation, connectivity, and intelligent decision-making in smart environments. Nevertheless, the problems of security, transparency, and control decentralization in resource-limited IoT networks remain major issues. A potential solution is blockchain technology; however, conventional consensus systems, such as Proof-of-Work and Proof-of-Stake, are not suitable for IoT applications because they are energy- and computationally intensive and not scalable. This research paper suggests an architecture of a secure and efficient automation of smart homes with real-time blockchain recording facilitated by a Lightweight Delegated Byzantine Fault Tolerance (LdBFT) protocol. The design provides solutions to major issues of the IoT system such as security, decentralization and scalability by proposing a lightweight consensus mechanism which is appropriate in resource constrained systems. The proposed study enhances the effectiveness of the IoT blockchain systems in terms of efficiency, security, and scalability. LdBFT protocol streamlines consensus functions with lightweight delegation and priority-based validation, which further guarantees low latency and energy usage as well as reliability and security in communication among distributed internet of thing devices in real-time smart homes. Experimental analysis demonstrates that the LdBFT protocol significantly reduces consensus wait time, increases fault tolerance against malicious or failed nodes, and consumes less energy than traditional blockchain protocols. Overall, the proposed system offers a scalable, resource-efficient, and secure blockchain architecture for use in smart home IoT settings.
Mark C. Ballandies, Florian Spychiger, Uwe Serdült, Claudio J. Tessone
We propose DAO-enabled decentralized physical AI (DePAI), a democratic architecture for coordinating humans and autonomous machines in the operation and governance of physical-digital systems. We (1) synthesize foundations in blockchains, decentralized autonomous organizations (DAOs), and cryptoeconomics; (2) connect DAO design with digital-democracy research on deliberation and voting, showing how each can advance the other; (3) position DAO-governed decentralized physical infrastructure networks (DePIN) within a vertically integrated stack that links energy and sensing to connectivity, storage/compute, models, and robots; (4) show how these elements specify workflows that couple machine execution with human oversight, enabling enhanced self-organization of techno-socio-economic systems, which we call DePAI; and (5) analyze risks, including security, centralization, incentive failure, legal exposure, and the crowding-out of intrinsic motivation, and argue for value-sensitive design and continuously adaptive governance. DePAI offers a path to scalable, resilient self-organization that integrates physical infrastructure, AI, and community ownership under transparent rules, on-chain incentives, and permissionless participation, aiming to preserve human autonomy.
This seminar paper presents a comprehensive study on blockchain interoperability, focusing on enabling communication between independent blockchain networks. It examines key techniques such as cross-chain bridges, atomic swaps, relay chains, and oracle-based solutions. The paper also analyzes major platforms like Polkadot, Cosmos, and Chainlink, highlighting their roles in improving scalability and efficiency. Additionally, it discusses the challenges, security concerns, and limitations of interoperability while proposing a hybrid framework to enhance secure and reliable cross-chain communication. The study emphasizes the importance of interoperability in advancing decentralized applications and the future of Web3 technologies.
The integration of Internet of Things (IoT) technologies into precision agriculture has transformed farming practices through real-time monitoring, automated irrigation, and data-driven decision-making. However, centralized access control mechanisms remain vulnerable to single points of failure, scalability issues, and security breaches such as spoofing and replay attacks. To address these challenges, this paper proposes a decentralized blockchain-based framework for access management in agricultural IoT environments. The architecture integrates IoT devices, edge gateways, smart contracts, and a distributed ledger to provide secure identity management, automated authorization, and immutable audit trails. Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) are combined to enable context-aware authorization, while Zero-Knowledge Proofs (ZKPs), Multi-Factor Authentication (MFA), and end-to-end encryption strengthen resilience against identity theft and unauthorized access. Performance evaluation demonstrates a$\mathbf{6 5 \%}$reduction in authentication latency and an access control accuracy of 98.5 %, confirming the framework's scalability and security effectiveness for large-scale precision agriculture deployments.
The Internet of Things (IoT) has become a major issue that has gained significant attention in the research community. Advances in IoT technologies have resulted in the emergence of various security issues and raised concerns about potential privacy breaches of IoT data. Utilizing Blockchain (BC) is seen as a promising solution for addressing security issues in the IoT. This paper offers a clear overview of IoT security threats, including the related security characteristics and the challenges that come with integrating BC with IoT. A brief discussion of various consensus protocols and existing security techniques is presented. A comparative study of several Distributed Ledger Technology (DLT) platforms based on both qualitative and quantitative evaluation criteria is also presented. This paper explores the role of BC Technology in improving security in Intrusion Detection Systems (IDS) and other applications in the IoT environment. Additionally, the paper identifies open issues and highlights potential research opportunities that can benefit future studies.
The rapid expansion of Artificial Intelligence Data Centers (AIDC) faces severe physical constraints, notably the linear O(n) scaling of power consumption, cooling requirements, and latency. In this paper, we propose the Lattice Swarm Protocol, a paradigm shift in distributed edge computing utilizing an O(1) constant memory architecture combined with the Virtual-to-Materialization (V2M) engine. We mathematically demonstrate that when interconnected via high-speed 400G/800G optical networks, multiple 1MW ultra-low-power edge nodes do not compute independently. Instead, they share Spatiotemporal Environmental Hashes across a 9192-D Lattice network. This mechanism exponentially reduces the computational load of the entire network as node count increases, creating a single 300MW-equivalent "Hyper-Organism" from merely 30 distributed 1MW nodes. We empirically validate this architecture through the implementation of zero-latency Stateless Custody protocols and interstellar acoustic materialization (Voyager 1), both audited by Google DeepMind Antigravity. This infrastructure establishes a new global standard for Autonomous Driving and Urban Air Mobility (UAM).Version 2 Update: Integrated Zero-Knowledge Proof (ZKP) mechanisms and Stateless Key Vaporization (0.024s), aligned with KIPO Patent No. 10-2026-0079266.
The rapid growth of geographically distributed cloud data centers has intensified the demand for secure, privacy-preserving, and resource-efficient collaboration among mutually untrusted cloud environments. This paper presents BlockFed, a blockchain-empowered federated learning framework designed to enable cyber-threat-resilient collaboration across geographically distributed data centers. In BlockFed, each data center independently trains local models using private workload data and shares only the computed gradients rather than raw data, with a centralized aggregation server through a secure blockchain layer. A Proof-of-Work consensus mechanism is employed to validate gradient transactions and maintain an immutable, tamper-resistant ledger, ensuring trust and integrity among participating entities. The centralized server aggregates blockchain-verified updates to construct a global model, which is iteratively redistributed to support collaborative learning. This integrated learning process enhances task-level resource demand prediction while simultaneously improving system security and operational efficiency across all participating data centers. Extensive simulations on the Google Cluster Dataset show that BlockFed outperforms state-of-the-art baselines, including ISTM, ETP-WE, First-Fit, Best-Fit, and Random-Fit, in resource utilization, power consumption, and active server reduction. BlockFed achieves up to 7-81% higher resource utilization, 41-58% lower power consumption, and 31-65% fewer active servers, while attaining a cyber-threat estimation accuracy of 93.19%.
Interoperability of patient files between hospitals continues to present significant obstacles. Health systems frequently utilize central EHR systems that could suffer malfunctions, data breaches, and unauthorized access by third parties. Not only does this jeopardize patient confidentiality, but it also hinders the efficient operations of hospital processes.Blockchain technology is viewed as a prospective remedy for the issue. Blockchain keeps its data differently, allowing users to store data securely and make changes difficult. In this study, we analyze research works published between 2016 and 2023 regarding blockchain-based hospital-to-hospital data exchange.The methodologies differ widely: there are cases where researchers use smart contracts in Ethereum, build a system on Hyperledger Fabric, and deploy IPFS. Moreover, certain studies incorporate encryption methods, machine learning algorithms, and more. In summary, the results show that blockchain allows for improved data protection and transparency while giving patients more control over their personal information. Still, some issues persist, such as scalability, expenses, integration with existing infrastructure, and adherence to GDPR and HIPAA requirements. For future work, more improvements are necessary. For instance, zero-knowledge proofs, cybersecurity measures for new technologies, and using artificial intelligence to audit and validate smart contracts may be promising solutions.
The fast evolution of large-scale Internet of Things (IoT) networks has made it possible to develop intelligent applications in smart cities, healthcare, industry automation, transport, and nextgeneration communication systems. However, the conventional centralized architecture and security mechanisms are seriously challenged regarding scalability, latency, energy efficiency, trust management, and resilience against cyber-attacks in large-scale and heterogeneous IoT networks.Blockchain and Distributed Ledger Technologies (DLTs) provide promising solutions with the help of decentralization, immutability, and secure data sharing. However, the conventional consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS) are not appropriate for large-scale IoT networks because of their high computational and communication complexities. This review provides a comprehensive analysis of the latest blockchain-based IoT solutions, with emphasis on lightweight, scalable, and energy-efficient consensus algorithms for largescale IoT networks. The latest and most advanced consensus algorithms, such as Delegated Proof of Stake (DPoS), Practical Byzantine Fault Tolerance (PBFT) variants, sharding-based systems, Directed Acyclic Graph (DAG)-based blockchain solutions, and agent-based DLT systems, are analyzed. The peer-reviewed literature is analyzed using quantitative performance analysis criteria such as throughput, latency, scalability, energy efficiency, and security resilience. The results show that although the new emerging lightweight and distributed ledger architectures have shown substantial improvements in performance and scalability in large-scale IoT networks, there are still important research challenges to be addressed in security robustness, interoperability, real-time processing, and real-world deployment validation. The research gaps are identified, and future research directions are presented in this review to develop new lightweight, secure, adaptive, and scalable blockchain-based architectures for next-generation large-scale IoT networks.