Search Results (1,519)

Digital twins are digital representations of physical entities that enable real-time updates through data transmission between the physical and virtual domains. Based on a cloud–edge–device framework, this paper investigates methods for real-time data transmission, processing, and storage during the polishing process of a belt grinding robot. On this basis, a digital twin monitoring framework is constructed for blade-specific belt grinding robots. First, a virtual robot model was constructed using a joint modeling workflow in SolidWorks 2025 and 3ds Max 2025, incorporating a lightweight high-fidelity mesh processing algorithm based on the QEM method. Second, a data acquisition and transmission architecture was proposed for the belt grinding robot, enabling data reading, writing, and real-time monitoring during machining, as well as establishing a cloud–edge–device database. Finally, a cloud–edge–device digital twin monitoring framework for the blade belt grinding robot was developed, based on real-time monitoring of grinding process data. This work establishes a foundational data acquisition and visualization platform for the blade grinding robot, providing the necessary cyber-physical infrastructure, which future predictive models can develop and validate. Full article

The LiDAR sensor embedded in recent iPhone Pro devices offers new opportunities for rapid, low-cost, and spatially detailed assessment of outdoor recreational infrastructure. Framed within mobile sensing and IoT-based environmental monitoring, this proof-of-concept case study evaluates a smartphone-based LiDAR workflow for localized hiking trail assessment under dense canopy conditions. Four independent surveys of a degraded hiking trail section were conducted to assess inter-survey repeatability, agreement with conventional field measurements, and practical field applicability. The resulting three-dimensional models reproduced trail morphology, including incised tread sections, exposed roots, rocky surfaces, and localized irregularities relevant to trail-condition assessment. Repeated registrations demonstrated consistent cloud-to-cloud comparison metrics, while comparisons with field reference measurements showed good agreement in the representation of cross-sectional morphology and exposed root characteristics. Continuous surface reconstruction additionally supported exploratory identification of potential runoff pathways and relative surface depressions. Although limited to a single trail section and one smartphone–application combination (iPhone 13 Pro with Scaniverse), the evaluated workflow demonstrates potential as a rapid, accessible, and relatively low-cost approach for localized trail-condition assessment and provides a foundation for further evaluation in hiking trail monitoring. Full article

The rapid integration of digital technologies including cloud infrastructures, Internet of Things ecosystems, artificial intelligence, and large-scale enterprise platforms has reshaped the operational and architectural foundations of contemporary organizations. Although these technologies enhance flexibility, scalability, and analytics-driven decision processes, they simultaneously increase system complexity, broaden attack surfaces, and intensify governance requirements. This manuscript adopts an engineering-oriented viewpoint on digital governance and enterprise cybersecurity, focusing on secure system architectures, policy-driven control mechanisms, and resilience-focused operational strategies. An integrated framework that unifies governance structures, quantitative risk assessment, compliance automation, cybersecurity engineering is introduced to enable secure-sustainable digital transformation across enterprise environments. Full article

Urban coastal cities face compounded flood hazards driven by sea-level rise, intense precipitation, and dense impervious surfaces. This study develops and demonstrates a cloud-native Python 3.12 GIS framework for flood susceptibility screening and critical facility exposure analysis in Miami, Florida, one of the most flood-exposed coastal cities in the United States. Defined here as a geospatial workflow that retrieves data dynamically from cloud-hosted APIs and executes entirely within a hosted computing environment, the framework integrates three open-source spatial indicators: terrain elevation from the USGS 3D Elevation Programme via py3dep; Euclidean distance to water bodies from OpenStreetMap via OSMnx; and building footprint density as an impervious surface proxy, also from OpenStreetMap. Indicators were standardised and combined using literature-informed MCDA weights (water proximity: 0.40; elevation: 0.35; building density: 0.25) into a continuous flood susceptibility index, classified at the 33rd- and 66th-percentile thresholds. In this proof-of-concept application, high-susceptibility zones cover 48.66 km² (34.0%) of the city, concentrated along coastal waterfronts and inland canal corridors. Overlaying critical facility locations on the classified surface indicates that 9 of 16 hospitals (56.2%), 61 of 244 schools (25.0%), and 5 of 17 fire stations (29.4%) fall within high-susceptibility zones; because this overlay uses centroid-based facility points that have not been cross-checked against official municipal or state facility registries, these counts should be read as indicative rather than definitive. Exact binomial testing shows that the school exposure deficit is statistically significant (p = 0.00), while elevated hospital exposure, although substantively notable, does not reach significance at the current sample size (p = 0.07). The susceptibility surface itself has not been quantitatively validated against external benchmarks such as FEMA flood maps or historical inundation records, the MCDA weights have not been sensitivity-tested, and spatial autocorrelation in the index has not been assessed; concrete protocols for each of these steps are specified as subsequent calibration work rather than as prerequisites for the architecture demonstrated here. The contribution of this paper is the reproducible, cloud-native workflow architecture and its proof-of-concept application, not a validated operational assessment tool; we present it explicitly as a methodological protocol and workflow demonstration, not as an evaluation of flood risk. The framework is fully reproducible, low-cost, and transferable to other US coastal cities. Full article

With the rapid development of Industrial Internet-oriented smart energy systems, hydro-turbine generator units are increasingly monitored through networked sensors, industrial communication infrastructures, and edge/cloud-based condition-monitoring platforms. These Internet-connected monitoring environments provide abundant vibration data for intelligent operation and maintenance (O&M) but also introduce a challenging unsupervised cross-scenario diagnosis problem. Specifically, diagnostic models trained on labeled historical data may suffer severe performance degradation when deployed to unlabeled online data collected under different hydraulic conditions, rotational speeds, or operating conditions. Furthermore, existing domain adaptation methods, in their pursuit of distribution alignment, frequently overlook a critical bottleneck that limits generalization performance: inter-class entanglement. Specifically, under intense hydraulic background noise and cross-condition distribution shifts, features belonging to distinct fault types are highly susceptible to aliasing within the feature space. To overcome these issues, this paper proposes a Conditional Wasserstein Adversarial Network with Bi-level Prototype Disentanglement Regularization (CWAN-BPDR). First, a Conditional Wasserstein Adversarial Network (CWAN) is constructed by combining the smooth-gradient property of Wasserstein distance with conditional adversarial alignment, thereby achieving stable and fine-grained category-level domain adaptation. Furthermore, to alleviate the inter-class entanglement problem that may arise during cross-domain alignment, a Bi-level Prototype Disentanglement Regularization (BPDR) term is designed. By jointly implementing source–target prototype alignment and prototype–feature bidirectional alignment, BPDR explicitly suppresses inter-class confusion and enhances intra-class compactness and inter-class separability in the feature space. Experimental results on the JNU and NEFU datasets demonstrate that CWAN-BPDR achieves average diagnostic accuracies of 97.82% and 98.99%, respectively, while significantly mitigating label entanglement in challenging cross-operating-condition tasks. These results indicate that the proposed method can effectively transfer diagnostic knowledge acquired from labeled historical operating conditions to unlabeled online monitoring data. It can therefore serve as an offline-trained diagnostic module for Industrial Internet of Things-based condition-monitoring platforms in hydropower systems. Full article

The rapid growth of AI-driven workloads, IoT ecosystems, and distributed digital services has exposed fundamental limitations in existing cloud and edge infrastructures, particularly in guaranteeing robust data privacy under emerging quantum threats. Current blockchain-based systems provide integrity and decentralization but rely predominantly on computational cryptography and access-control mechanisms, leaving them vulnerable to future quantum adversaries and large-scale inference attacks. In this paper, we introduce Data Communities as a novel paradigm for privacy-preserving, blockchain-enabled cooperative digital infrastructures, formalized within the Cooperative Digital Infrastructure (CDI) framework. Our approach integrates three complementary privacy protection layers: (i) MuReQua, a quantum-resilient blockchain consensus mechanism leveraging CQKD for cryptographic robustness against Shor-type attacks; (ii) DeSSE, an information-theoretically secure distributed storage model based on n × m fragmentation, ensuring zero information leakage below reconstruction thresholds; and (iii) a multi-tier data sovereignty model (C0–C3) enforcing policy-driven data locality and regulatory compliance across federated nodes. We formalize privacy guarantees through an adversarial model encompassing classical, quantum, insider, and governance-level threats, and demonstrate that the proposed architecture achieves information-theoretic confidentiality, forward secrecy, and attack-resilient distributed governance. A privacy leakage analysis shows that the probability of data reconstruction under sub-threshold compromise is identical to zero, outperforming conventional blockchain storage models based on encryption alone. Simulation and case study results indicate that Data Communities achieve up to 99.999% service availability, 55% reduction in external data exposure, and 22–35% carbon-aware optimization, while maintaining strict privacy guarantees across distributed environments. Compared with existing blockchain systems (e.g., Ethereum, Hyperledger Fabric), the proposed framework shifts privacy protection from access-control and pseudonymity to structural, information-theoretic privacy by design. Overall, the results establish Data Communities as a scalable and quantum-resilient foundation for next-generation privacy-preserving blockchain infrastructures, bridging distributed AI, secure storage, and cooperative governance under a unified formal model. Full article

Cardiovascular diseases are among the leading causes of mortality worldwide, making continuous electrocardiographic (ECG) monitoring essential for the early detection and follow-up of cardiac abnormalities. This study presents an Internet of Things (IoT) platform for continuous remote monitoring of three-lead ECG signals using LTE-M cellular connectivity. The proposed system integrates an ADS1293 analog front-end for ECG acquisition, a Walter IoT Module for MQTT-based data transmission, and a cloud infrastructure for secure data reception, storage, and visualization. A dual-core implementation on the ESP32 separates ECG acquisition from MQTT communication, preventing sample loss during continuous monitoring. Experimental results demonstrated reliable transmission of raw three-lead ECG signals, with no packet loss observed during a continuous 12 h LTE-M transmission test involving approximately 250,000 packets. The monitoring mechanism also automatically detected packet losses and device restarts. At the same time, server performance evaluation showed low CPU and memory utilization during simultaneous communication with five data sources under the evaluated conditions. These results validate the proposed IoT communication platform and establish a foundation for future work, including intelligent ECG analysis, improvements in signal acquisition and conditioning, and the continued development of the portable Holter device. Full article

The rapid digital transformation of education has increased the demand for intelligent assessment systems and architecture capable of evaluating institutional readiness for technology-enhanced teaching, learning, and workforce development. As educational organizations adopt digital platforms, cloud-based learning environments, and globally connected instructional models, there is a growing need for systematic frameworks that can assess human, technological, and organizational capabilities required for successful implementation. This study proposes a digital capability assessment framework for technology-enhanced educational systems that integrates instructional competency evaluation, technology readiness analysis, infrastructure assessment, and institutional support monitoring within a unified analytics-driven model. The proposed framework consists of multiple assessment components, including digital literacy measurement, technology integration capability analysis, instructional innovation indicators, collaborative learning readiness metrics, and institutional resource evaluation mechanisms. These components are designed to support continuous monitoring of digital transformation initiatives and provide evidence-based decision support for educational planning, resource allocation, and technology adoption strategies. The framework further incorporates analytics and reporting functions that enable stakeholders to identify capability gaps, evaluate implementation risks, and prioritize system improvement initiatives. To demonstrate the applicability of the framework, a pilot assessment was conducted using competency and readiness data collected from instructional personnel within a technology-enhanced educational environment. Analytical results revealed strong capability levels across digital instructional practices, technology-supported curriculum development, online learning delivery, and collaborative knowledge-sharing activities. The assessment also identified infrastructure and support-related constraints that may affect the scalability and sustainability of advanced digital learning initiatives. The proposed framework contributes a scalable architecture for institutional readiness assessment and digital capability analytics within technology-enhanced educational systems. By integrating human capability indicators, infrastructure readiness measures, and organizational support metrics into a unified evaluation model, the framework provides a foundation for intelligent decision-support systems, digital transformation monitoring platforms, and technology governance mechanisms in modern educational ecosystems. Full article

The digitalization of photovoltaic (PV) systems can support sustainable energy deployment by improving operational efficiency, system visibility, and energy extraction. However, many existing Internet of Things (IoT)-enabled solutions address monitoring and maximum power point tracking (MPPT) separately or depend on proprietary platforms, remote cloud services, and relatively costly hardware, which may restrict their accessibility and replication in small-scale and resource-constrained applications. This study presents the implementation and laboratory-scale experimental evaluation of an edge-IoT architecture that integrates real-time PV monitoring, embedded adaptive MPPT control, local data management, and visualization using low-cost hardware and open-source software. The proposed architecture combines an ESP32 microcontroller with a Raspberry Pi (RPi) local server to enable environmental and electrical sensing, edge-based control, message queuing telemetry transport (MQTT) communication, local data storage, and interactive visualization through the open-source Node-RED, InfluxDB, and Grafana platforms. An adaptive perturb-and-observe (AP&O) algorithm is implemented on the ESP32 to dynamically adjust the duty cycle of a DC–DC boost converter in response to changing operating conditions. The system is experimentally evaluated using a PV test bench equipped with a custom boost converter and sensing modules measuring eleven electrical and environmental parameters. The architecture achieved an average communication latency of 193 ± 23 ms and an average MPPT efficiency of 97.3 ± 0.54%. It also provided a power gain of 0.7 ± 0.5% compared with the conventional fixed-step perturb-and-observe method. By combining local processing, open-source software, low-cost components, and integrated monitoring and control, the proposed system reduces dependence on external cloud infrastructure while supporting responsive and accessible PV energy management. These results demonstrate its potential as a replicable technological framework for improving the operational sustainability and digital management of small-scale PV installations. Full article

Forests cover nearly one-third of the Earth’s land surface and are subject to increasing anthropogenic pressure, including unauthorised construction, infrastructure expansion, and habitat fragmentation. Detecting buildings concealed beneath forest canopy is essential for environmental monitoring and territorial surveillance, yet optical satellite imagery fails under persistent cloud cover and dense vegetation, and no existing approach provides reliable detection across contrasting forest ecosystems. We address this gap by proposing a hybrid SAR-based detection framework that combines twelve physically interpretable Scattering-Informed SAR Features (SISF)—derived from electromagnetic scattering theory across amplitude, polarimetric, temporal, and texture dimensions—with a universal Convolutional Neural Network, integrated through probability-level fusion with isotonic regional calibration. Rather than relying on individual feature thresholds, the framework identifies buildings through their characteristic multidimensional scattering signature—a combination that remains discriminative across biomes where any single SAR descriptor would fail. The framework was evaluated on a novel 7721-object multi-biome benchmark spanning forest-steppe (Kazakhstan), boreal forest (Komi Republic, Russia), and tropical rainforest (Brazil, Pará). The final Fusion + Regional Calibration model achieved an overall F1-score of 0.803 on the held-out test set (N = 1545), outperforming single-model baselines by up to 17 percentage points. Regional F1-scores ranged from 0.727 (Kazakhstan) to 0.944 (Komi Republic), with detection performance remaining robust under partial canopy occlusion (F1 = 0.911, versus 0.903 for unobscured buildings). An empirical inverse relationship between hard negative proportion and detection F1-score was identified within each biome, with the 28–35% proportions present in our sampled regions reported as a preliminary observation rather than a generally optimal range—a dataset design finding not previously reported in the literature. The proposed framework provides a physically interpretable solution for SAR-based building detection under forest canopy, demonstrating consistent performance across three contrasting forest biomes, with direct applications to environmental monitoring and territorial surveillance in forested regions. Full article

Preparing training data for domain-specific medical Named Entity Recognition (NER) involves a trade-off between annotation quality, expert effort, and data privacy: manual annotation is costly, whereas cloud-based Large Language Models (LLMs) raise concerns about the control of sensitive clinical text. This article introduces NERFlow, a workflow-driven subsystem of the FIT4NER project whose contribution is an abstraction layer that makes rule-based, model-based, and LLM-based annotation interchangeable and comparable within one configurable workflow environment. Open-source LLMs are integrated as exchangeable annotation services, deployable locally or in cloud-agnostic infrastructures via Kubernetes, and embedded into the KM-EP knowledge management system. NERFlow was evaluated qualitatively, through a cognitive walkthrough, an IEEE 1028 technical review, and a user-centered survey with 18 participants, and quantitatively on the CRAFT corpus with seven open-source and hosted LLMs run through an identical pipeline. The results support its use as LLM-assisted pre-annotation with expert correction, with locally deployable open-source models as the more reliable basis for reproducible operation. Full article

Field-based infrastructure inspection often occurs under intermittent or absence connectivity, limiting the applicability of cloud-dependent analytical systems. Existing approaches primarily focus on detection accuracy or edge processing, but rarely address the integration of reliable analytics, data traceability, and deployment constraints within a unified framework. To address this gap, we present OffInspect-LLM, an offline inspection platform integrating controlled LLM-assisted analytical interaction. The system combines an inspection pipeline for object detection with a constrained natural language interface that generates validated SQL queries, ensuring safe and traceable data interaction. Experimental evaluation on a dataset of 1600 images demonstrated stable multi-seed held-out test performance, achieving a deployment-oriented mean held-out test mAP@50:95 of 0.617 across five random seeds, while the highest exploratory single-run validation result reached 0.714 under fixed initialization conditions. The primary deployment-oriented evaluation corresponds to the multi-seed held-out test performance rather than the peak single-run validation result. In addition to predictive performance, the system achieves per-image inference times below 3 s on GPU, reliable batch processing, and scalable geospatial visualization exceeding 10,000 detections. The primary contribution lies in the integration of detection, structured data management, and audited querying within an offline-first architecture, enabling traceable and deployment-oriented inspection workflows through constrained analytical interaction under realistic operational conditions. Full article

This paper presents the design and preliminary field validation of a portable, low-cost Internet of Things (IoT) station for georeferenced soil nutrient profiling in agricultural environments. The proposed system integrates a digital RS-485 NPK soil sensor, an ESP32 microcontroller, and a SIM7000G GSM/GPS module to enable on-site acquisition and real-time transmission of nitrogen (N), phosphorus (P), and potassium (K) measurements using the MQTT protocol. Data are serialized in JSON format and transmitted to a ThingsBoard cloud platform for remote storage and visualization. The portable architecture supports manual spatial sampling across multiple locations without reliance on fixed infrastructure, making it suitable for small- and medium-scale agricultural contexts with limited connectivity. Preliminary testing in a controlled lemon plantation demonstrated stable GSM connectivity, successful geotagging, and consistent cloud-based visualization, with an average acquisition–transmission cycle of 30–45 s per measurement. Spatial heat maps generated from collected data illustrate the system’s capability for indicative nutrient mapping. Although laboratory-grade validation is ongoing, the results confirm the technical feasibility of integrating low-cost sensing, cellular communication, and georeferenced data acquisition into a compact IoT unit. The system establishes a foundation for future calibration, large-scale field validation, and decision-support applications in precision agriculture. Full article

Project-Based Learning (PBL) has become an important pedagogical strategy for developing technical and professional competencies in engineering education through authentic, multidisciplinary experiences. This paper presents a PBL case study conducted in an undergraduate Computer Engineering course in which students designed and implemented Smart Fisio Borregos, an Internet of Things (IoT) and Artificial Intelligence (AI) system designed as both a learning vehicle for students and a prototype tool intended to support rehabilitation training through real-time exercise guidance and monitoring; the educational effectiveness of the rehabilitation-support function has not yet been formally validated. The project followed a prototype-driven methodology that integrated low-cost sensors, embedded systems, cloud databases, computer vision, and AI services into a unified platform. The resulting prototype incorporated equipment occupancy monitoring, environmental control, RFID-based access management, a web dashboard for data visualization, and a computer-vision module based on MediaPipe Pose Landmarker for exercise analysis and feedback. The project provided students with opportunities to apply knowledge from programming, embedded systems, databases, networking, and AI while developing collaboration, problem-solving, and project-management skills. The paper describes the pedagogical framework, system architecture, implementation process, and project outcomes, illustrating how multidisciplinary engineering projects can be used to create authentic learning experiences connected to real-world challenges. The proposed approach offers a replicable model for integrating IoT and AI technologies into engineering curricula while contributing to educational innovation related to health and well-being. The study aligns with Sustainable Development Goal 3 (Good Health and Well-Being) and Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). Full article

Water quality management in large-scale institutional infrastructures faces significant challenges due to the high latency of manual sampling and the energy–connectivity trade-offs in traditional IoT deployments. This paper proposes HydroSense AI, a robust three-tier IoT framework designed for real-time multi-parameter water quality monitoring and predictive analytics. The system integrates a heterogeneous sensing layer (pH, TDS, turbidity, and temperature) with a hybrid communication architecture, utilising Long Range (LoRa) technology for low-power transmission over long ranges (manufacturer-rated for line-of-sight distances of up to 16 km, and validated up to 2 km within a dense campus environment in this study), bridged via an ESP32-based gateway to the cloud. To address the critical issue of energy autonomy in remote sensing nodes, we implement a hardware-synchronised duty-cycling mechanism using a DS3231 Real-Time Clock (RTC), enabling precise deep-sleep scheduling and significantly extending battery operational life. Beyond data acquisition, the framework incorporates AI-driven trend-forecasting and anomaly-detection models to provide early warnings of water degradation through a Telegram-integrated alert system. Experimental validation over an extended deployment period demonstrates high measurement stability, with the forecasting model achieving a one-step (10-min) normalised RMSE of 0.0063 (equivalent to 0.033 pH units) for pH and 0.0298 (17.0 ppm) for TDS on a held-out test partition; a benchmark against persistence and ARIMA baselines is also provided. A complete measured energy decomposition of the deployed node is reported: hardware-synchronised duty cycling reduces the quiescent current to 18.2 μA, and with a 12 s acquisition window at 112 mA on a 10-min cycle, the mean current is 2.26 mA, corresponding to an estimated 46 days of unattended operation on a 2500 mAh cell. Critically, the acquisition window accounts for 99.2% of the per-cycle energy budget and the sleep interval for only 0.8%, so quiescent current—the figure of merit most often reported as evidence of low-power design—is shown not to be the binding constraint for sensor-dominated nodes of this class. The results indicate that the proposed hybrid architecture offers a 99.8% packet delivery ratio for sustainable water management. Full article