Search Results (398)

The General Data Protection Regulation (GDPR) imposes additional demands and obligations on service providers that handle and process personal data. In this paper, we examine how advanced cryptographic techniques can be employed to develop a privacy-preserving solution for ensuring GDPR compliance in Industrial Internet of Things (IIoT) systems. The primary objective is to ensure that sensitive data from IIoT devices is encrypted and accessible only to authorized entities, in accordance with Article 32 of the GDPR. The proposed system combines Decentralized Attribute-Based Encryption (DABE) with smart contracts on a blockchain to create a decentralized way of managing access to IIoT systems. The proposed system is used in an IIoT use case where industrial sensors collect operational data that is encrypted according to DABE. The encrypted data is stored in the IPFS decentralized storage system. The access policy and IPFS hash are stored in the blockchain’s smart contracts, allowing only authorized and compliant entities to retrieve the data based on matching attributes. This decentralized system ensures that information is stored encrypted and secure until it is retrieved by legitimate entities, whose access rights are automatically enforced by smart contracts. The implementation and evaluation of the proposed system have been analyzed and discussed, showing the promising achievement of the proposed system. Full article

Severe water scarcity has become a critical constraint to global agricultural development. Enhancing both the timeliness and accuracy of crop evapotranspiration (ET_c) retrieval is essential for optimizing irrigation scheduling. Addressing the limitations of conventional ground-based point-source measurements in rapidly acquiring two-dimensional ET_c information at the field scale, this study employed unmanned aerial vehicle (UAV) remote sensing equipped with multispectral and thermal infrared sensors to obtain high spatiotemporal resolution imagery of a representative citrus orchard (Citrus reticulata Blanco cv. ‘Yichangmiju’) in western Hubei at different phenological stages. In conjunction with meteorological data (air temperature, daily net radiation, etc.), ET_c was retrieved using two established approaches: the Seguin-Itier (S-I) model, which relates canopy–air temperature differences to ET_c, and the multispectral-driven single crop coefficient method, which estimates ET_c by combining vegetation indices with reference evapotranspiration. The thermal-infrared-driven S-I model, which relates canopy–air temperature differences to ET_c, and the multispectral-driven single crop coefficient method, which estimates ET_c by combining vegetation indices with reference evapotranspiration. The findings indicate that: (1) both the S-I model and the single crop coefficient method achieved satisfactory ET_c estimation accuracy, with the latter performing slightly better (accuracy of 80% and 85%, respectively); (2) the proposed multi-source fusion model consistently demonstrated high accuracy and stability across all phenological stages (R² = 0.9104, 0.9851, and 0.9313 for the fruit-setting, fruit-enlargement, and coloration–sugar-accumulation stages, respectively; all significant at p < 0.01), significantly enhancing the precision and timeliness of ET_c retrieval; and (3) the model was successfully applied to ET_c retrieval during the main growth stages in the Cangwubang citrus-producing area of Yichang, providing practical support for irrigation scheduling and water resource management at the regional scale. This multi-source fusion approach offers effective technical support for precision irrigation control in agriculture and holds broad application prospects. Full article

The Internet of Things (IoT) has enabled a vast network of devices to communicate over the Internet. However, the fragmentation of IoT systems continues to hinder seamless data sharing and coordinated management across platforms.However, there is currently no actual search engine for IoT data. Existing IoT search engines are considered device discovery tools, providing only metadata about devices rather than enabling access to IoT application data. While efforts such as IoTCrawler have striven to support IoT application data, they have largely failed due to the fragmentation of IoT systems and the heterogeneity of IoT data.To address this, we recently introduced SensorsConnect—a unified framework designed to facilitate interoperable content and sensor data sharing among collaborative IoT systems, inspired by how the World Wide Web (WWW) enabled shared and accessible information spaces for humans. This paper presents the IoT Agentic Search Engine (IoTASE), a real-time semantic search engine tailored specifically for IoT environments. IoTASE leverages LLMs and Retrieval-Augmented Generation (RAG) techniques to address the challenges of navigating and searching vast, heterogeneous streams of real-time IoT data. This approach enables the system to process complex natural language queries and return accurate, contextually relevant results in real time. To evaluate its effectiveness, we implemented a hypothetical deployment in the Toronto region, simulating a realistic urban environment using a dataset composed of 500 services and over 37,000 IoT-like data entries. Our evaluation shows that IoT-ASE achieved 92% accuracy in retrieving intent-aligned services and consistently generated concise, relevant, and preference-aware responses, outperforming generalized outputs produced by systems such as Gemini. These results underscore the potential of IoT-ASE to make real-time IoT data both accessible and actionable, supporting intelligent decision-making across diverse application domains. Full article

Background and Clinical Significance: Retained intrarenal foreign bodies are rare adverse events after endourological stone surgery. Guidewire fracture or detachment is uncommon and can trigger infection, obstruction, or encrustation if unrecognized. We report antegrade percutaneous snare retrieval of a retained hydrophilic guidewire tip and provide a concise literature review (seven PubMed-indexed intrarenal cases identified by a structured search) to inform diagnosis, management, and prevention. We also clarify the clinical rationale for an antegrade versus retrograde approach and the sequencing of decompression, definitive stone management, and stenting in the context of sepsis. Case Presentation: A 75-year-old woman with diabetes presented with obstructive left pyelonephritis from ureteral and renal calculi. After urgent percutaneous nephrostomy, she underwent semirigid and flexible ureteroscopic lithotripsy with double-J stenting; the nephrostomy remained. During routine tube removal, the stent was inadvertently extracted. Seven days later she re-presented with fever and flank pain. KUB and non-contrast CT showed a linear 4 cm radiopaque foreign body in the left renal pelvis with dilatation. Under local anesthesia and fluoroscopy, a percutaneous tract was used to deploy a 35 mm gooseneck snare and retrieve the distal tip of a hydrophilic guidewire (Sensor/ZIP-type). Inflammatory markers were normalized; the nephrostomy was removed on day 5; six-week imaging confirmed complete clearance without complications. Conclusions: Retained guidewire fragments should be suspected in postoperative patients with unexplained urinary symptoms or infection. Cross-sectional imaging confirms the diagnosis, while minimally invasive extraction—preferably an antegrade percutaneous approach for rigid or coiled fragments—achieves prompt resolution. This case adds to the seven prior PubMed-indexed intrarenal reports identified in our review, bringing the total to eight, underscoring prevention through pre-/post-use instrument checks, immediate fluoroscopy when withdrawal resistance occurs, and structured device accounting to avoid “never events.” Full article

The proliferation of sensor technology has led to an explosion in data volume, making the retrieval of specific information from large repositories increasingly challenging. While Retrieval-Augmented Generation (RAG) can enhance Large Language Models (LLMs), they often lack precision in specialized domains. Taking the Civil IoT Taiwan Data Service Platform as a case study, this study addresses this gap by developing a dialogue engine enhanced with a GraphRAG framework, aiming to provide accurate, context-aware responses to user queries. Our method involves constructing a domain-specific knowledge graph by extracting entities (e.g., ‘Dataset’, ‘Agency’) and their relationships from the platform’s documentation. For query processing, the system interprets natural language inputs, identifies corresponding paths within the knowledge graph, and employs a recursive self-reflection mechanism to ensure the final answer aligns with the user’s intent. The final answer transformed into natural language by utilizing the TAIDE (Trustworthy AI Dialogue Engine) model. The implemented framework successfully translates complex, multi-constraint questions into executable graph queries, moving beyond keyword matching to navigate semantic pathways. This results in highly accurate and verifiable answers grounded in the source data. In conclusion, this research validates that applying a GraphRAG-enhanced engine is a robust solution for building intelligent dialogue systems for specialized data platforms, significantly improving the precision and usability of information retrieval and offering a replicable model for other knowledge-intensive domains. Full article

Nowadays, Internet of Things (IoT) application systems are broadly applied to various sectors of society for efficient management by monitoring environments using sensors, analyzing sampled data, and giving proper feedback. For their fast deployment, we have developed Smart Environmental Monitoring and Analysis in Real Time (SEMAR) as an integrated IoT application server platform and implemented the input setup assistance service using prompt engineering and a generative AI model to assist connecting sensors to SEMAR with step-by-step guidance. However, the current service cannot assist in connections of the sensors not learned by the AI model, such as newly released ones. To address this issue, in this paper, we propose an extension to the service for handling unlearned sensors by utilizing datasheets with four steps: (1) users input a PDF datasheet containing information about the sensor, (2) key specifications are extracted from the datasheet and structured into markdown format using a generative AI, (3) this data is saved to a vector database using chunking and embedding methods, and (4) the data is used in Retrieval-Augmented Generation (RAG) to provide additional context when guiding users through sensor setup. Our evaluation with five generative AI models shows that OpenAI’s GPT-4o achieves the highest accuracy in extracting specifications from PDF datasheets and the best answer relevancy (0.987), while Gemini 2.0 Flash delivers the most balanced results, with the highest overall RAGAs score (0.76). Other models produced competitive but mixed outcomes, averaging 0.74 across metrics. The step-by-step guidance function achieved a task success rate above 80%. In a course evaluation by 48 students, the system improved the student test scores, further confirming the effectiveness of our proposed extension. Full article

The climatic conditions of a region are a constant object of study, especially now that climate change is clearly affecting quality of life and the way we live. The study of the climatic conditions of a region is conducted through meteorological data. Meteorological installations include a set of sensors to monitor the meteorological and climatic conditions of an area. Meteorological data parameters include measurements of temperature, humidity, precipitation, wind speed, and direction, as well as tools such as an oratometer and a pyranometer, etc. Specifically, the pyranometer is a high-cost instrument, which has the ability to measure the intensity of the sunshine on the surface of the earth, expressing the measurement in Watt/m². Pyranometers have many applications. They can be used to monitor solar energy in a given area, in automated systems such as photovoltaic system management, or in automatic building shading systems. In this research, both the implementation and the evaluation of an integrated low-cost pyranometer system is presented. The proposed pyranometer device consists of affordable modules, both microprocessor and sensor. In addition, a central server, as the information system, was created for data collection and visualization. The data from the measuring system is transmitted via a wireless network (Wi-Fi) over the Internet to an information system (central server), which includes a database for collecting and storing the measurements, and visualization software. The end user can retrieve the information through a web page. The results are encouraging, as they show a satisfactory degree of determination of the measurements of the proposed low-cost device in relation to the reference measurements. Finally, a correction function is presented, aiming at more reliable measurements. Full article

This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article

The reliable operation of rotating machinery is critical in industrial production, necessitating advanced fault diagnosis and maintenance strategies to ensure operational availability. This study employs supervised machine learning algorithms to apply multi-label classification for fault detection in rotating machinery, utilizing a real dataset from multi-sensor systems installed on a suction fan in a typical manufacturing industry. The presented system focuses on multi-modal data analysis, such as vibration analysis, temperature monitoring, and ultrasound, for more effective fault diagnosis. The performance of general machine learning algorithms such as kNN, SVM, RF, and some boosting techniques was evaluated, and it was shown that the Random Forest achieved the best classification accuracy. Feature importance analysis has revealed how specific domain characteristics, such as vibration velocity and ultrasound levels, contribute significantly to performance and enabled the detection of multiple faults simultaneously. The results demonstrate the machine learning model’s ability to retrieve valuable information from multi-sensor data integration, improving predictive maintenance strategies. The presented study contributes a practical framework in intelligent fault diagnosis as it presents an example of a real-world implementation while enabling future improvements in industrial condition-based maintenance systems. Full article

This paper proposes a general workflow for collecting and analyzing open data from Bicycle Sharing Systems (BSSs) that was developed using data from the Valenbisi system, operated in Valencia by the French company JCDecaux; however, the stages of the proposed workflow are service-independent and can be applied broadly. Cycling has become an increasingly popular mode of transportation, leading to the emergence of BSSs in modern cities. Parallel to this, Smart City solutions have been implemented using Internet of Things (IoT) technologies, such as embedded sensors and GPS-based communication systems, which have become essential to everyday life. When public transportation services or bicycle sharing systems are used, real-time information about the services is provided to customers, including vehicle tracking based on GPS technology and the availability of bikes via sensors installed at bike rental stations. The bike stations were examined from two different perspectives: first, their daily usage, and second, the types of facilities located in their surroundings. Based on these two approaches, the overlap between the clustering results was analyzed—specifically, the similarity in how stations could be grouped and the correlation between their usage and locations. To enhance the raw data retrieved from the service provider’s official API, the stations were annotated based on OpenStreetMap and Overpass API data. Data visualization was created using Tableau from Salesforce. Based on the results, an agreement of 62% was found between the results of the two different clustering approaches. Full article

The Flexible Combined Imager (FCI) instrument aboard the Meteosat Third Generation (MTG-I) geostationary satellite, launched in December 2022 and operational since September 2024, by providing shortwave infrared (SWIR), medium infrared (MIR) and thermal infrared (TIR) data, with an image refreshing time of 10 min and a spatial resolution ranging between 500 m in the high-resolution (HR) and 1–2 km in the normal-resolution (NR) mode, may represent a very promising instrument for monitoring thermal volcanic activity from space, also in operational contexts. In this work, we assess this potential by investigating the recent Mount Etna (Italy, Sicily) eruption of February–March 2025 through the analysis of daytime and night-time SWIR observations in the NR mode. The time series of a normalized hotspot index retrieved over Mt. Etna indicates that the effusive eruption started on 8 February at 13:40 UTC (14:40 LT), i.e., before information from independent sources. This observation is corroborated by the analysis of the MIR signal performed using an adapted Robust Satellite Technique (RST) approach, also revealing the occurrence of less intense thermal activity over the Mt. Etna area a few hours before (10.50 UTC) the possible start of lava effusion. By analyzing changes in total SWIR radiance (TSR), calculated starting from hot pixels detected using the preliminary NHI algorithm configuration tailored to FCI data, we inferred information about variations in thermal volcanic activity. The results show that the Mt. Etna eruption was particularly intense during 17–19 February, when the radiative power was estimated to be around 1–3 GW from other sensors. These outcomes, which are consistent with Multispectral Instrument (MSI) and Operational Land Imager (OLI) observations at a higher spatial resolution, providing accurate information about areas inundated by the lava, demonstrate that the FCI may provide a relevant contribution to the near-real-time monitoring of Mt. Etna activity. The usage of FCI data, in the HR mode, may further improve the timely identification of high-temperature features in the framework of early warning contexts, devoted to mitigating the social, environmental and economic impacts of effusive eruptions, especially over less monitored volcanic areas. Full article

Arc scanning synthetic aperture radar (ArcSAR) can achieve high-resolution panoramic imaging and retrieve submillimeter-level deformation information. To monitor buildings in a city scenario, ArcSAR must be lightweight; have a high resolution, a mid-range (around a hundred meters), and low power consumption; and be cost-effective. In this study, a novel high-resolution wide-beam single-chip millimeter-wave (mmwave) ArcSAR system, together with an imaging algorithm, is presented. First, to handle the non-uniform azimuth sampling caused by motor motion, a high-accuracy angular coder is used in the system design. The coder can send the radar a hardware trigger signal when rotated to a specific angle so that uniform angular sampling can be achieved under the unstable rotation of the motor. Second, the ArcSAR’s maximum azimuth sampling angle that can avoid aliasing is deducted based on the Nyquist theorem. The mathematical relation supports the proposed ArcSAR system in acquiring data by setting the sampling angle interval. Third, the range cell migration (RCM) phenomenon is severe because mmwave radar has a wide azimuth beamwidth and a high frequency, and ArcSAR has a curved synthetic aperture. Therefore, the fourth-order RCM model based on the range-Doppler (RD) algorithm is interpreted with a uniform azimuth angle to suit the system and implemented. The proposed system uses the TI 6843 module as the radar sensor, and its azimuth beamwidth is 64°. The performance of the system and the corresponding imaging algorithm are thoroughly analyzed and validated via simulations and real data experiments. The output image covers a 360° and 180 m area at an azimuth resolution of 0.2°. The results show that the proposed system has good application prospects, and the design principles can support the improvement of current ArcSARs. Full article

With the development of artificial intelligence and intelligent sensor technologies, traditional legal English teaching approaches have faced numerous challenges in handling multimodal inputs and complex reasoning tasks. In response to these challenges, a cross-modal legal English question-answering system based on visual and acoustic sensor inputs was proposed, integrating image, text, and speech information and adopting a unified vision–language–speech encoding mechanism coupled with dynamic attention modeling to effectively enhance learners’ understanding and expressive abilities in legal contexts. The system exhibited superior performance across multiple experimental evaluations. In the assessment of question-answering accuracy, the proposed method achieved the best results across BLEU, ROUGE, Precision, Recall, and Accuracy, with an Accuracy of 0.87, Precision of 0.88, and Recall of 0.85, clearly outperforming the traditional ASR+SVM classifier, image-retrieval-based QA model, and unimodal BERT QA system. In the analysis of multimodal matching performance, the proposed method achieved optimal results in Matching Accuracy, Recall@1, Recall@5, and MRR, with a Matching Accuracy of 0.85, surpassing mainstream cross-modal models such as VisualBERT, LXMERT, and CLIP. The user study further verified the system’s practical effectiveness in real teaching environments, with learners’ understanding improvement reaching 0.78, expression improvement reaching 0.75, and satisfaction score reaching 0.88, significantly outperforming traditional teaching methods and unimodal systems. The experimental results fully demonstrate that the proposed cross-modal legal English question-answering system not only exhibits significant advantages in multimodal feature alignment and deep reasoning modeling but also shows substantial potential in enhancing learners’ comprehensive capabilities and learning experiences. Full article

Text-based image retrieval is one of the most common approaches for searching images acquired from vision sensors such as cameras. However, this method suffers from limitations in retrieval accuracy, particularly when the query contains limited information or involves previously unseen sentences. These challenges arise because keyword-based matching fails to adequately capture contextual and semantic meanings. To address these limitations, we propose a novel approach that transforms sentences and images into semantic graphs and scene graphs, enabling a quantitative comparison between them. Specifically, we utilize a graph neural network (GNN) to learn features of nodes and edges and generate graph embeddings, enabling image retrieval through natural language queries without relying on additional image metadata. We introduce a contrastive GNN-based framework that matches semantic graphs with scene graphs to retrieve semantically similar images. In addition, we incorporate a hard negative mining strategy, allowing the model to effectively learn from more challenging negative samples. The experimental results on the Visual Genome dataset show that the proposed method achieves a top nDCG@50 score of 0.745, improving retrieval performance by approximately 7.7 percentage points compared to random sampling with full graphs. This confirms that the model effectively retrieves semantically relevant images by structurally interpreting complex scenes. Full article

Satellite remote sensing provides a cost-effective and large-scale alternative to traditional methods for retrieving water quality parameters for inland waters. Effective water quality parameter retrieval via optical satellite remote sensing requires three key components: (1) a sensor whose measurements are sensitive to variations in water quality; (2) accurate atmospheric correction to eliminate the effect of absorption and scattering in the atmosphere and retrieve the water-leaving radiance/reflectance; and (3) a bio-optical model used to estimate water quality from the optical signal. This study provides a literature review and an evaluation of these three components. First, a review of decommissioned, active, and upcoming satellite sensors is presented, highlighting their advantages and limitations, and a ranking method is introduced to assess their suitability for retrieving chlorophyll-a, colored dissolved organic matter, and non-algal particles in inland waters. This ranking can aid in selecting appropriate sensors for future studies. Second, the strengths and weaknesses of atmospheric correction algorithms used over inland waters are examined. The results show that no atmospheric correction algorithm performed consistently across all conditions. However, understanding their strengths and weaknesses allows users to select the most suitable algorithm for a specific use case. Third, the challenges, limitations, and recent advances of machine learning use in bio-optical models for inland water quality parameter retrieval are discussed. Machine learning models have limitations, including low generalizability, low dimensionality, spatial/temporal autocorrelation, and information leakage. These issues highlight the importance of locally trained models, rigorous cross-validation methods, and integrating auxiliary data to enhance dimensionality. Finally, recommendations for promising research directions are provided. Full article