Search Results (1,299)

Industrial IoT maintenance often stops at health prediction, leaving maintenance, rescheduling, safety, and communication to separate decision processes. This study presents MIRA, a safety-constrained graph-based multi-agent reinforcement learning architecture for joint prescriptive maintenance, production rescheduling, and event-triggered communication. Machine condition was estimated from CNC milling data using temporal convolutional models; because predictive uncertainty failed a predefined validation gate, the controller used deterministic health estimates. Evaluation covered five controllers, six simulated scenarios, and 1800 matched episodes. Relative to Graph-MAPPO, MIRA reduced operational cost by 9.38%, weighted tardiness by 28.10%, unexpected failures by 17.39%, message count by 84.98%, and transmitted data by 83.83%, while increasing on-time completion by 23.55%, without a detectable difference in corrected critical-message recall. Across the three independently trained seeds, failures, safety violations, and message count favored MIRA consistently, whereas cost and tardiness favored MIRA in two seeds. Disabling the execution shield increased safety violations from 0 to 3.56 per episode. Post-training variation in the projected-health safe-start threshold from 0.124 to 0.132 produced no safety violations and only small changes in aggregate operational outcomes. Cross-domain health transfer to PHM 2010 failed without adaptation. The results support simulator-level decision coordination, while broader replication, variable-size deployment, and factory validation remain necessary. Full article

Background: Population growth and rising healthcare demand increasingly strain public healthcare in emerging economies. Further, poor supply visibility, frequent stock-outs, and medicine expiry weaken healthcare supply chain performance (SCP) and patient outcomes. In response, the Internet of Things (IoT) offers promising ways to improve the monitoring, distribution, and management of medical supplies. However, empirical evidence on how technological, organisational, and environmental factors influence IoT adoption and its effect on public healthcare SCP remains limited. This study examined factors influencing IoT adoption and its effect on supply chain performance in public healthcare facilities. Methods: Data were collected from 102 respondents drawn from 90 public healthcare facilities. Results: Factors associated with technological factors have the most significant influence on the adoption of IoT in public healthcare SCs. The lack of significance of organisational and environmental factors may be attributed to the early stage of IoT adoption in public healthcare SCs. Conclusions: This study contributes to the theoretical understanding of IoT adoption by highlighting the dominant role of technological factors over organisational and environmental considerations in resource-constrained public healthcare settings. From a practical perspective, the findings encourage policymakers and healthcare managers to prioritise investments in relevant ICT infrastructure to accelerate IoT adoption. Full article

Ground-level ozone (${\mathrm{O}}_3$) is a secondary photochemical pollutant whose formation under intense Mediterranean solar radiation makes Greece prone to elevated concentrations, with well-documented respiratory and cardiovascular health effects. Forecasting of ozone in Greece has nonetheless remained limited: prior work has been largely statistical, focused on one or two sites in Athens, and restricted to a single horizon, and no study has compared machine learning forecasts across the urban-to-rural gradient of the national monitoring network. This study addresses that gap with, to our knowledge, the first multi-site comparison of machine learning ozone forecasts spanning the full urban-to-rural typology gradient of a Greek monitoring network, introducing a typology × model accuracy cross-matrix that links site character directly to forecasting performance and offers concrete, horizon-specific operational guidance for early warning deployment. We develop a single, reproducible pipeline and compare three model families, Support Vector Regression (SVR), Random Forest (RF) and gradient boosting (GBM), against a naïve persistence baseline, for next-hour and next-day-maximum ${\mathrm{O}}_3$ prediction at eight urban, suburban and rural stations of the National Air Pollution Monitoring Network (2021–2024). All predictors are derived from the ozone series itself (lagged, rolling and cyclical calendar features); models were tuned and evaluated using a strictly chronological 70/15/15 train/validation/test split per station, with hyperparameters selected on the validation partition and the test partition evaluated only once to prevent temporal leakage. At the next-hour horizon, all models outperformed persistence at every site, with the single exception of SVR at the rural THR site; gradient boosting was best or joint-best (test $R^2$ from 0.83 at the heavily titrated central-urban site to 0.94 at a rural site), and a Wilcoxon signed-rank test on paired per-sample errors confirmed that GBM significantly outperformed Random Forest at all seven evaluated stations ($p<0.05$, and $p<0.001$ at six of them). The next-day-maximum task was substantially harder ($R^2$ between 0.41 and 0.79), and the ranking reversed, with SVR being the best model almost everywhere. Crucially, forecast accuracy declined systematically from rural and suburban toward central-urban sites: the same nitric oxide titration that suppresses urban ozone is also associated with degraded predictability. These results, summarised in the typology × model accuracy cross-matrix, indicate that an operational early warning system for Greece should adopt horizon-specific models, gradient boosting for next-hour nowcasting and SVR for day-ahead maxima, with explicitly higher uncertainty in the urban core, and can be deployed on the network’s real-time (IoT) data stream using ozone observations alone. Full article

In IoT health monitoring using electrocardiograms (ECGs), the surge in data transmission volume poses a significant challenge. This study utilized PhysioNet ECG data to compare the transmission volumes of raw ECG signals, R-R intervals (RRIs), and HRV metrics (SDNN, RMSSD, and LF/HF), thereby evaluating the effectiveness of communication optimization. The results demonstrated that transmitting RRI data and HRV metrics reduced data volume by approximately 99% and over 99.9%, respectively, compared to transmitting raw ECG data. These results suggest that the proposed approach could contribute to improved energy efficiency and reduced transmission latency in wearable devices, supporting its potential feasibility for bandwidth-constrained IoMT deployments. Full article

This article presents a systematic review of Internet of Things technology applications across the entire lifecycle of prefabricated buildings. By combining bibliometric analysis with qualitative research methods, seven key research topics in this field are identified and analyzed: integrated information management platforms, position tracking, quality checking and management, project management and cost control, carbon emissions monitoring, indoor environment monitoring, and data security and information encryption. The current research status and challenges pertaining to each of these topics are critically assessed with emphasis on the main challenges in terms of automation level and accuracy, system integration and data interoperability, and deployment economy and robustness. The findings reveal that current IoT applications in prefabricated buildings are mainly focused on data collection, data visualization, and status monitoring, and future research should further strengthen the integration of IoT with AI, big data, and other technologies to promote predictive analysis, intelligent optimization, and autonomous decision-making. Three key topics are subsequently discussed from a management perspective: collaborative carbon information flow management, human-centered health and safety management, and finally, smart operation, maintenance, and disassembly driven by a circular economy, and directions are proposed for their future integration and innovation with emerging technologies. This study provides directional recommendations and references for researchers and practitioners in related fields. The review further suggests that the future development of IoT-enabled prefabricated buildings requires not only technological breakthroughs but also the collaborative evolution of digital technologies, construction practices, and industrial systems, supported by effective management mechanisms, industry collaboration, and practical implementation strategies. Full article

Recently, the Internet of Things (IoT) has evolved into the Artificial Intelligence of Things (AIoT) through its combination with artificial intelligence (AI) technology and has become capable of providing intelligent services in all industrial sectors. Globally, energy consumption within buildings is continuously increasing alongside the advancement of IT and AI technologies. Since this increase is attributed to various causes—ranging from large-scale climate change to small-scale indoor environmental factors (air quality) and health factors—research aimed at reducing indoor energy consumption is actively underway. In particular, in the home environment where people spend a significant portion of their day, maintaining indoor air quality (IAQ) is critical for health, and energy conservation in heating, ventilation, and air conditioning (HVAC) systems is essential. In Korea, the number of single-person households is increasing and was expected to reach 36.1% of all households by 2024, leading people to live in increasingly smaller homes. This study aimed to verify residents using contactless facial recognition to prevent pandemics such as COVID-19 and to provide comfortable indoor air quality. Resident facial recognition was performed by identifying residents’ faces in images captured by the Pi camera using OpenCV’s Haar feature-based cascade classifier. Indoor air quality measurements were conducted in four indoor locations, measuring various environmental factors (PM_2.5, CO₂, etc.) based on environmental sensors and the IoT. Furthermore, to manage indoor air quality, AI was utilized based on the measurement data to classify the four spaces, with a success rate of 96%. Additionally, considering the indoor area of the experimental environment (97 m²), it was confirmed that operating a 70 W air purifier only when the resident is indoors can reduce power consumption by approximately 33–75% compared to running it 24 h a day. Full article

Accurate pavement crack segmentation is essential for structural health monitoring, yet existing methods often face a trade-off between segmentation accuracy and computational efficiency. To address this issue, a novel teacher–student framework, termed RTCS-T and RTCS-S, is proposed. The teacher network RTCS-T is constructed based on the Swin Transformer to capture long-range dependencies and multi-scale contextual information. To further enhance crack representation, a strip refinement module is introduced to model directional structural features, while a cascaded atrous spatial pyramid pooling module is employed to improve multi-scale feature aggregation. Based on the teacher network, a lightweight student model RTCS-S is developed by using depthwise separable convolutions to achieve efficient inference. In addition, a foreground-aware and boundary-aware knowledge distillation strategy is introduced to guide the transfer of structural and contextual information from the teacher to the student. Experiments on the Crack500, DeepCrack, and CFD datasets demonstrated competitive performance against representative segmentation models. On CFD, RTCS-S achieved an F1 Score of 0.7514 and an mIoU of 0.7962. Notably, RTCS-S required only 1.82 M parameters and 1.13 GFLOPs and achieved a model inference speed of 680 FPS on an RTX 4090 GPU. When deployed on an RDK X5 edge-computing platform, the complete pipeline achieved an end-to-end throughput of 34 FPS, with an average latency of approximately 29.4 ms and peak memory consumption of 1.8 GB. These results demonstrate that the proposed framework provides an efficient solution for automated pavement crack detection and shows strong potential for practical road inspection applications. Full article

Grassland and pasture management increasingly requires timely and reliable decision support to address changing environmental conditions, optimize grazing capacity, and improve herbage production. Although digital technologies such as the Internet of Things (IoT), Cloud Computing, Digital Twins, Artificial Intelligence (AI) and Machine Learning (ML) have been widely adopted, they are often implemented as isolated solutions rather than as an integrated management framework. This paper proposes a cloud-based smart grazing framework that combines field monitoring, biomass forecasting, digital twin monitoring, and stocking optimization within a unified architecture. The framework includes two interconnected algorithms: the first supports the operational grazing management cycle through IoT sensing, biomass forecasting, digital twin monitoring, and stocking optimization, while the second enables secure ML training and model updating for biomass prediction, livestock health assessment, and grazing behavior analysis. To ensure data integrity with low computational overhead, the framework employs SHA-256 hash-based verification rather than a full blockchain implementation. The proposed architecture provides a practical approach for integrating monitoring, prediction, and secure data management to support sustainable grazing management. 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

Biogenic amines (BAs) are nitrogenous compounds formed by microbial decarboxylation of amino acids in protein-rich foods. Their accumulation indicates spoilage and poses health risks. Traditional methods like high-performance liquid chromatography (HPLC) and gas chromatography (GC) are sensitive but time-consuming, limiting on-site use. Rapid technologies based on specific recognition molecules offer feasible alternatives for real-time monitoring. This review summarizes five categories of recognition elements: antibodies, aptamers, molecularly imprinted polymers (MIPs), enzymes, and peptides for BA detection in foods. These elements convert BA concentrations into optical, electrical, or colorimetric signals, establishing a complete biosensing chain. Integration with portable platforms (lateral flow assays (LFAs), microfluidic chips, smart labels, and smartphone devices) is also discussed. Recognition-element-based sensing enables high-selectivity and rapid monitoring of BAs in foods. Antibody/aptamer systems excel in specific histamine detection, enzyme platforms in rapid total amine assessment, and MIPs in chemical stability and matrix tolerance. Yet practical application is limited by poor selectivity for similar amines, matrix interference, insufficient real-food validation, and device standardization. Our future focus will be on AI-assisted design, multi-target arrays, smartphone quantification, and IoT-enabled freshness monitoring. Full article

Predictive maintenance of electric vehicle (EV) fleets requires accurate estimation of Remaining Useful Life (RUL), Time-to-Failure (TTF), and State-of-Health (SOH) from heterogeneous Internet of Things (IoT) telemetry. However, real-world degradation patterns are nonlinear, nonstationary, and highly imbalanced near failure. This study proposes Q-TRACNet, a temporal attention framework that combines causal maintenance-aware preprocessing, adaptive temporal condensation, residual refinement, learnable phase modulation, and hybrid Particle Swarm Optimization–Quantum-Guided Descent parameter tuning. The framework is evaluated on the EV-HLM-RUL dataset and three established prognostics benchmarks: NASA CMAPSS, PHM 2012, and XJTU-SY. Chronological training, validation, and testing partitions are used to preserve temporal causality. On EV-HLM-RUL, Q-TRACNet achieves an MAE of 9.8, an RMSE of 14.7, an $R^2$ of 0.979, and a Critical Degradation Awareness Index (CDAI) of 0.91. It reduces RMSE by 20.11% relative to the strongest competing baseline and achieves an NRMSE of 0.102 and a Kendall correlation of 0.89 ($p<{10}^{-4}$). Cross-dataset experiments demonstrate stable performance for RUL, TTF, and short- and long-horizon SOH prediction. Ablation and sensitivity analyses further confirm the contributions of the temporal and attention components and the stability of degradation-aware evaluation. Q-TRACNet also provides lower training cost and inference latency than competing architectures, supporting practical maintenance planning, inspection prioritization, and resource allocation in connected EV fleets. Full article

Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem. Full article

The effects of climate change have altered precipitation patterns, causing extreme weather events that impact crop yields and increase the demand for agricultural production, which drives the need to implement emerging technologies. This research presents the design, construction, and validation of a real-time monitoring system for environmental parameters in open-field avocado orchards. A four-phase methodology was employed: (1) Establishment of required parameters and construction of the IoT architecture; (2) Design of the geometry and final elaboration of the system: modeling in SolidWorks, circuit diagrams in Fritzing, and system assembly; (3) Development of a mobile application: Android Studio and development of the autoencoder model with machine learning; and (4) Validation: Evaluation of the wireless link; system implementation and deployment of the mobile application. The results obtained include a system with a transmitter node equipped with sensors and a receiver, both incorporating ESP32 and nRF24L01+PA+LNA modules, with a wireless transmission range of 2300 m. A total of 2016 data records were stored on microSD and in the cloud, which can be queried, visualized, and analyzed through the mobile application via Bluetooth or Wi-Fi; the system also detects outlier data that can be used for decision-making in the agricultural sector. Full article

Physiological signal analysis using wearable biosensors like an electroencephalogram (EEG) and an electrocardiogram (ECG) is widely investigated for affective computing; however, the integration of deep learning-based affective computing within 6G-driven IoT healthcare infrastructures remains limited, with data transmission latency posing a significant challenge. This study proposes a framework for EEG and ECG-based affective state analysis over 6G IoT networks. We utilize an attention-based deep learning model for three-class emotion recognition from EEG signals, and a ResNet50-based convolutional neural network for three-class stress/affective state classification using ECG data. The framework’s communication performance is evaluated through ray tracing simulations in a virtual hospital environment at 7 GHz and 92 GHz bands. Experimental results on SEED and WESAD datasets demonstrate that the EEG model achieved 90.93% classification accuracy, while the ECG model yielded 82.26% validation and 65.64% test accuracy. Wireless analysis showed RMS delay spread values of 6.53 ns (7 GHz) and 3.32 ns (92 GHz), with correlation bandwidths of 30.63 MHz and 60.24 MHz, respectively. These findings demonstrate the feasibility of integrating wearable biosensor-based affective state analysis with 6G-oriented IoT healthcare communication frameworks, providing a robust foundation for future personalized health and human state monitoring applications. Full article

A huge effort has been directed towards research and development of new measurement systems for maximizing comfort and safety in public buildings by monitoring indoor air quality (IAQ). In fact, according to World Health Organization, exposure to chemical, biological, and physical agents in poorly ventilated spaces can lead to psycho-physical discomfort as well as respiratory and neurological diseases. Recent advances in the Internet of Things (IoT) have paved the ground for the design and implementation of distributed measurement systems with higher sensor density and computational capacity. While these systems provide accurate assessments of individual rooms, they do not account for personal exposure to varying air quality levels over time. In public buildings such as schools, universities, and workplaces, occupants frequently move between rooms according to predefined schedules, resulting in heterogeneous exposure patterns. To address this issue, this paper proposes an innovative IAQ measurement technique for public buildings, shifting the focus from room-based assessment to occupant-centered assessment. Unlike wearable or portable personal monitors, the proposed technique infers occupant location from the institutional timetable and combines it with the fixed sensor infrastructure already installed in the rooms, requiring no additional devices to be worn. Individual conditions are quantified through a new personalized metric that integrates instantaneous air quality, cumulative individual exposure over time, and thermal comfort into a single index that is evaluated against occupant-specific thresholds. The technique is validated using real-world data, demonstrating higher potential to ensure safety and comfort compared to the state of the art. Full article