Search Results (159)

Industrial Internet of Things (IIoT) networks, Microsoft Azure Internet of Things (IoT) Hub, and device twins (DvT) are increasingly recognized as core enablers of adaptive, data-driven manufacturing. This paper proposes a hierarchical IIoT framework that integrates industrial IoT networking, DvT for asset-level virtualisation, system-level digital twins (DT) for cell orchestration, and cloud-native services to support the digital transformation of brownfield, programmable logic controller (PLC)-centric modular automation (MA) environments. Traditional PLC/supervisory control and data acquisition (SCADA) paradigms struggle to meet interoperability, observability, and adaptability requirements at scale, motivating architectures in which DvT and IoT Hub underpin real-time orchestration, virtualisation, and predictive-maintenance workflows. Building on and extending a previously introduced conceptual model, the present work instantiates a multilayered, end-to-end design that combines a federated Message Queuing Telemetry Transport (MQTT) mesh on the on-premises side, a ZigBee-based backup mesh, and a secure bridge to Azure IoT Hub, together with a systematic DvT modelling and orchestration strategy. The methodology is supported by a structured analysis of relevant IIoT and DvT design choices and by a concrete implementation in a nine-cell MA laboratory featuring a robotic arm predictive-maintenance scenario. The resulting framework sustains closed-loop monitoring, anomaly detection, and control under realistic workloads, while providing explicit envelopes for telemetry volume, buffering depth, and latency budgets in edge-cloud integration. Overall, the proposed architecture offers a transferable blueprint for evolving PLC-centric automation toward more adaptive, secure, and scalable IIoT systems and establishes a foundation for future extensions toward full DvT ecosystems, tighter artificial intelligence/machine learning (AI/ML) integration, and fifth/sixth generation (5G/6G) and time-sensitive networking (TSN) support in industrial networks. Full article

This study provides a systematic overview of Natural Language Processing (NLP)-based frameworks for Cyber Threat Intelligence (CTI) and the early prediction of cyberattacks in Industry 4.0. As digital transformation accelerates through the integration of IoT, SCADA, and cyber-physical systems, manufacturing environments face an expanding and complex cyber threat landscape. Following the PRISMA 2020 systematic review protocol, 80 peer-reviewed studies published between 2015 and 2025 were analyzed across IEEE Xplore, Scopus, and Web of Science to identify methods that employ NLP for CTI extraction, reasoning, and predictive modelling. The review finds that transformer-based architectures, knowledge graph reasoning, and social media mining are increasingly used to convert unstructured data into actionable intelligence, thereby enabling earlier detection and forecasting of cyber threats. Large Language Models (LLMs) demonstrate strong potential for anticipating attack sequences, while domain-specific models enhance industrial relevance. Persistent challenges include data scarcity, domain adaptation, explainability, and real-time scalability in operational-technology environments. The review concludes that NLP is reshaping Industry 4.0 cybersecurity from reactive defense toward predictive, adaptive, and intelligence-driven protection, and it highlights the need for interpretable, domain-specific, and resource-efficient frameworks to secure Industry 4.0 ecosystems. Full article

The opaque nature of black-box deep learning (DL) models poses significant challenges for intrusion detection systems (IDSs) in Internet of Things (IoT) networks, where transparency, trust, and operational reliability are critical. Although explainable artificial intelligence (XAI) has been increasingly adopted to enhance interpretability, its impact on detection performance and computational efficiency in resource-constrained IoT environments remains insufficiently understood. This systematic review investigates the performance of an explainable deep learning-based IDS for IoT networks by analyzing trade-offs among detection accuracy, computational overhead, and explanation quality. Following the PRISMA methodology, 129 peer-reviewed studies published between 2018 and 2025 are systematically analyzed to address key research questions related to XAI technique trade-offs, deep learning architecture performance, post-deployment XAI evaluation practices, and deployment bottlenecks. The findings reveal a pronounced imbalance in existing approaches, where high detection accuracy is often achieved at the expense of computational efficiency and rigorous explainability evaluation, limiting practical deployment on IoT edge devices. To address these gaps, this review proposes two conceptual contributions: (i) an XAI evaluation framework that standardizes post-deployment evaluation categories for explainability, and (ii) the Unified Explainable IDS Evaluation Framework (UXIEF), which models the fundamental trilemma between detection performance, resource efficiency, and explanation quality in IoT IDSs. By systematically highlighting performance–efficiency gaps, methodological shortcomings, and practical deployment challenges, this review provides a structured foundation and actionable insights for the development of trustworthy, efficient, and deployable explainable IDS solutions in IoT ecosystems. Full article

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is enabling a new class of intelligent applications. Specifically, Large Language Models (LLMs) are emerging as powerful tools not only for natural language understanding but also for enhancing IoT security. However, the integration of these computationally intensive models into resource-constrained IoT environments presents significant challenges. This paper provides an in-depth examination of how LLMs can be adapted to secure IoT ecosystems. We identify key application areas, discuss major challenges, and propose optimization strategies for resource-limited settings. Our primary contribution is a novel collaborative embeddings offloading mechanism for IoT intrusion detection named SEED (Semantic Embeddings for Efficient Detection). This system leverages a lightweight, fine-tuned BERT model, chosen for its proven contextual and semantic understanding of sequences, to generate rich network embeddings at the edge. A compact neural network deployed on the end-device then queries these embeddings to assess network flow normality. This architecture alleviates the computational burden of running a full transformer on the device while capitalizing on its analytical performance. Our optimized BERT model is reduced by approximately 90% from its original size, now representing approximately 41 MB, suitable for the Edge. The resulting compact neural network is a mere 137 KB, appropriate for the IoT devices. This system achieves 99.9% detection accuracy with an average inference time of under 70 ms on a standard CPU. Finally, the paper discusses the ethical implications of LLM-IoT integration and evaluates the resilience of LLMs in dynamic and adversarial environments. Full article

Wireless power transfer (WPT) technologies are advancing rapidly, yet their development trajectories within specific regional contexts remain underexplored. This review synthesizes India’s contributions to both near-field and far-field WPT research. We conducted a systematic literature survey spanning 2018–2024 to identify dominant technological themes, benchmark performance against global standards, and analyze innovation patterns within India’s research ecosystem. The review reveals a consistent focus on robust, cost-effective, and context-appropriate designs across both domains. In near-field WPT, Indian research emphasizes misalignment-tolerant magnetic coupling and high-frequency power converters for applications including electric vehicle charging and biomedical implants. In far-field WPT, progress is evident in rectenna architectures that enhance angular coverage and efficiency, particularly for IoT networks. We consolidate quantitative performance metrics from the literature to establish reference benchmarks and delineate persistent research gaps. We propose a forward-looking research agenda aimed at aligning WPT innovation with India’s sustainable development goals and energy accessibility challenges. This analysis provides a foundation for understanding how regional ecosystems shape technological priorities and offers insights for global WPT development. Full article

The increasing integration of Artificial Intelligence (AI) in education (AIEd) and its dependence on contemporary communication infrastructures (5G/6G, the Internet of Things (IoT), and Multi-Access Edge Computing (MEC)) has prompted a surge of research into applications, infrastructural dependencies, and deployment constraints. This is giving rise to a new paradigm termed AI-Enabled Telecommunication-Based Education (AITE). This review synthesises the recent literature (2022–2025) to examine how telecommunications and AI technologies converge to enhance educational ecosystems through adaptive learning systems, intelligent tutoring systems, AI-driven assessment, and administration. The findings reveal that low-latency, high-bandwidth connectivity, combined with edge-deployed analytics, enables real-time personalisation, continuous feedback, and scalable learning models that extend beyond traditional classrooms. In addition, persistent critical challenges are also reported, including issues with ethical governance, data privacy, algorithmic fairness, and uneven access to digital infrastructure, all affecting equitable adoption. By linking pedagogical transformation with telecom performance metrics—namely, latency, Quality of Service (QoS), and device interconnectivity—this work outlines a unified cross-layer framework for AITE. This review concludes by identifying future research avenues in ethical AI deployment, resilient architectures, and inclusive policy design to ensure transparent, secure, and human-centred educational transformation. Full article

In the context of green transition and digital transformation, forestry is becoming a strategic area of application of current modern technologies. The Internet of Things (IoT), artificial intelligence (AI), big data analysis (Big Data) and Digital Twins define the basic infrastructure of smart forestry. By connecting sensors, drones and satellites, IoT allows for continuous monitoring of forest ecosystems, risk anticipation and decision optimization in real-time. The purpose of this study is to perform a comprehensive narrative analysis of the relevant scientific literature from the recent period (2020–2025) regarding the application of IoT in forestry, highlighting the conceptual, technological and institutional developments. Based on a selection of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (29 full-text articles), four major axes are analyzed: (A) forest fire detection and prevention; (B) climate-smart forestry and carbon accounting; (C) forest digitalization through the concepts of Forest 4.0, Forest 5.0 and Digital Twins; (D) sustainability and digital forest policies. The results show that IoT is a catalyst for the sustainable transformation of the forest sector, supporting carbon accounting, climate-risk reduction and data-driven governance. The analysis highlights four major developments: the consolidation of IoT–AI architectures, the integration of IoT and remote sensing, the emergence of Forest 4.0/5.0 and Digital Twins and the growing role of governance and data standards. These findings align with the objectives of the EU Forest Strategy 2030 and the European Green Deal. Full article

The proliferation of Internet of Things (IoT) applications in safety-critical domains, such as healthcare, smart transportation, and industrial automation, demands robust solutions for data integrity, traceability, and security that surpass the capabilities of centralized databases. This paper analyzes how blockchain technology can be integrated with core IoT service functions—including data management, security, device management, group coordination, and automated billing—to enhance immutability, trust, and operational efficiency. Our analysis identifies practical use cases such as consensus-driven tamper-proof storage, role-based access control, firmware integrity verification, and automated micropayments. These use cases showcase blockchain’s potential beyond traditional data storage. Building on this, we propose a novel framework that integrates a permissioned distributed ledger with a standardized IoT service layer platform through a Blockchain Interworking Proxy Entity (BlockIPE). This proxy dynamically maps IoT service functions to smart contracts, enabling flexible data routing to conventional databases or blockchains based on the application requirements. We implement a Dockerized prototype that integrates a C-based oneM2M platform with an Ethereum-compatible permissioned ledger (implemented using Hyperledger Besu) via BlockIPE, incorporating security features such as role-based access control. For performance evaluation, we use Ganache to isolate proxy-level overhead and scalability. At the proxy level, the blockchain-integrated path achieves processing latencies (≈86 ms) comparable to, and slightly faster than, the traditional database path. Although the end-to-end latency is inherently governed by on-chain confirmation (≈0.586–1.086 s), the scalability remains high (up to 100,000 TPS). This validates that the architecture secures IoT ecosystems with manageable operational overhead. Full article

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments. Full article

Blockchain has been widely proposed to strengthen data management security through decentralization, immutability, and auditable transactions, capabilities increasingly recognized as enablers of sustainable digital ecosystems and resilient institutions; however, existing studies remain dispersed across domains and rarely consolidate governance, interoperability, and evaluation criteria. This paper conducts a systematic literature review of 70 peer-reviewed studies published between 2018 and 2024, using IEEE Xplore, Scopus, Springer, ScienceDirect, and ACM Digital Library as primary sources and following Kitchenham’s guidelines and the PRISMA 2020 flow, to examine how blockchain has been applied to secure data in healthcare, IoT, smart cities, supply chains, and cloud environments. The analysis identifies four methodological streams—empirical implementations, cryptographic/security protocols, blockchain–machine learning integrations, and conceptual frameworks—and shows that most contributions are technology-driven, with limited attention to standard metrics, regulatory compliance, and cross-platform integration. In addition, the review reveals that very few works articulate governance models that align technical solutions with organizational policies, which creates a gap for institutions seeking trustworthy, auditable, and privacy-preserving deployments. The review contributes a structured mapping of effectiveness criteria (confidentiality, auditability, availability, and compliance) and highlights the need for governance models and interoperable architectures to move from prototypes to production systems. Future work should prioritize large-scale validations, policy-aligned blockchain solutions, and comparative evaluations across sectors. Full article

The Internet of Things (IoT) has established an exceptional ecosystem of interconnected devices where a vast multitude of heterogeneous devices can communicate, collect, and share data for enhanced decision-making processes. To effectively analyze this immense volume of data, researchers have deployed advanced machine learning algorithms and deep neural networks. However, these approaches typically rely on centralized data storage for training, which raises significant privacy concerns. Federated Learning (FL) addresses this issue by allowing devices to train local models on their own data and share only model updates. Despite this advantage, FL remains vulnerable to several security threats, including model poisoning, data manipulation, and Byzantine attacks. Therefore, robust and scalable authentication mechanisms are essential to ensure secure participation in FL environments. This study provides a comprehensive survey of authentication in FL. We examine the authentication process, discuss the associated key challenges, and analyze architectural considerations relevant to securing FL deployments. Existing authentication schemes are reviewed and evaluated in terms of their effectiveness, limitations, and practicality. To provide deeper insight, we classify these schemes along two dimensions as follows: their underlying enabling technologies, such as blockchain, cryptography, and AI-based methods, and the system contexts in which FL operates. Furthermore, we analyze the datasets and experimental environments used in current research, identify open research challenges, and highlight future research directions. To the best of our knowledge, this study presents the first structured and comprehensive analysis of authentication mechanisms in FL, offering a foundational reference for advancing secure and trustworthy federated learning systems. Full article

The proliferation of Internet of Things (IoT) devices in financial markets has created distributed ecosystems where cryptocurrency exchanges, trading platforms, and market data providers operate as autonomous edge nodes generating massive volumes of sensitive financial data. Collaborative machine learning across these distributed financial IoT nodes faces fundamental challenges: institutions possess valuable proprietary data but cannot share it directly due to competitive concerns, regulatory constraints, and trust management requirements in decentralized networks. This study presents a privacy-preserving federated learning framework tailored for distributed financial IoT systems, combining differential privacy with Shamir secret sharing to enable secure collaborative intelligence across blockchain-based cryptocurrency trading networks. We implement per-layer gradient clipping and Rényi differential privacy composition to minimize utility loss while maintaining formal privacy guarantees in edge computing scenarios. Using 5.6 million orderbook observations from 11 cryptocurrency pairs collected across distributed exchange nodes, we evaluate three data partitioning strategies simulating realistic heterogeneity patterns in financial IoT deployments. Our experiments reveal that federated edge learning imposes 9–15 percentage point accuracy degradation compared to centralized cloud processing, driven primarily by data distribution heterogeneity across autonomous nodes. Critically, adding differential privacy (ε = 3.0) and cryptographic secret sharing increases this degradation by less than 0.3 percentage points when mechanisms are calibrated appropriately for edge devices. The framework achieves 62–66.5% direction accuracy on cryptocurrency price movements, with confidence-based execution generating 71–137 basis points average profit per trade. These results demonstrate the practical viability of privacy-preserving collaborative intelligence for distributed financial IoT while identifying that the federated optimization gap dominates privacy mechanism costs. Our findings offer architectural insights for designing trustworthy distributed systems in blockchain-enabled financial IoT ecosystems. Full article

The rapid evolution of platform technologies is transforming industries, interoperability, and innovation. Despite numerous studies on individual technologies, no prior review unifies AI, IoT, blockchain, and 5G with cross-sector standards, governance, and technical enablers to provide a comprehensive view of platform convergence. This narrative review synthesizes conceptual and technical literature from 2015–2025, focusing on how converging platform technologies interact across sectors. The review organizes findings by technological enablers, cross-domain integration mechanisms, sector-specific applications, and emergent trends, highlighting systemic synergies and challenges. The study demonstrates that AI, IoT, blockchain, cloud-edge architectures, and advanced communication networks collectively enable interoperable, secure, and adaptive ecosystems. Key enablers include standardized protocols, edge–cloud orchestration, and cross-platform data sharing, while challenges involve cybersecurity, regulatory compliance, and scalability. Sectoral examples span healthcare, finance, manufacturing, smart cities, and autonomous systems. Platform convergence offers transformative potential for sustainable and intelligent systems. Critical research gaps remain in unified architectures, privacy-preserving AI and blockchain mechanisms, and dynamic orchestration of heterogeneous systems. Emerging technologies such as quantum computing and federated learning are poised to further strengthen collaborative ecosystems. This review provides actionable insights for researchers, policymakers, and industry leaders aiming to harness platform convergence for innovation and sustainable development. Full article

Automated monitoring systems increasingly leverage diverse sensing sources, yet a disconnect often persists between machine vision and IoT sensor pipelines. While IoT sensors provide reliable point measurements and cameras offer rich spatial context, their independent operation limits coherent environmental interpretation. Existing multimodal fusion frameworks frequently lack tight synchronization and efficient cross-modal learning. This paper introduces a unified edge–cloud framework that deeply integrates cameras as active sensing nodes within an IoT network. Our approach features tight time synchronization between visual and IoT data streams and employs cross-modal knowledge distillation to enable efficient model training on resource-constrained edge devices. The system leverages a multi-task learning setup with dynamically adjusted loss weighting, combining architectures like EfficientNet, Vision Transformers, and U-Net derivatives. Validation on environmental monitoring tasks, including classification, segmentation, and anomaly detection, demonstrates the framework’s robustness. Experiments deployed on compact edge hardware (Jetson Nano, Coral TPU) achieved 94.8% classification accuracy and 87.6% segmentation quality (mIoU), and they also sustained sub-second inference latency. The results confirm that the proposed synchronized, knowledge-driven fusion yields a more adaptive, context-aware, and deployment-ready sensing solution, significantly advancing the practical integration of machine vision within IoT ecosystems. Full article

The global increase in dementia cases, which is predicted to exceed 152 million by 2050, poses substantial challenges to healthcare systems and caregiving structures. Concurrently, the expansion of mobile health (mHealth) technologies offers scalable, cost-effective opportunities for dementia care. This study systematically reviews 100 publicly available dementia-related mobile applications on the Apple App Store (iOS) and the Google Play Store (Android), categorised using the Mobile App Rating Scale (MARS), as well as the targeted end-users, Internet of Things (IoT) integration, data protection, and cost burden. Applications were evaluated for their utility in cognitive training, memory support, carer education, clinical decision-making, and emotional well-being. Findings indicate a predominance of carer resources and support tools, while clinically integrated platforms, cognitive assessments, and adaptive memory aids remain underrepresented. Most apps lack empirical validation, inclusive design, and integration with electronic health records, raising ethical concerns around data privacy, transparency, and informed consent. In parallel, the study identifies promising pathways for energy-optimised IoT systems, Artificial Intelligence (AI), and Ambient Assisted Living (AAL) technologies in fostering dementia-friendly, sustainable environments. Key gaps include limited use of low-power wearables, energy-efficient sensors, and smart infrastructure tailored to therapeutic needs. Application domains such as cognitive training (19 apps) and carer resources (28 apps) show early potential, while emerging innovations in neuroadaptive architecture and emotional computing remain underexplored. The findings emphasize the need for co-designed, evidence-based digital solutions that align with the evolving needs of people with dementia, carers, and clinicians. Future innovations must integrate sustainability principles, promote interoperability, and support global aging populations through ecologically responsible, person-centred dementia care ecosystems. Full article