Search Results (481)

Telemedicine, driven by the Internet of Things (IoT) and wireless connectivity, is essential for managing cardiovascular diseases, where hypertension remains the primary risk factor. In preclinical research, rabbits are superior biological models compared to rodents due to their human-like lipid metabolism. However, continuous blood pressure monitoring in this species remains challenging. The gold-standard technique (direct carotid catheterization) requires terminal procedures, and indirect methods (Doppler, oscillometric) show limited agreement with direct measurements. Furthermore, commercially available implantable telemetry platforms, while enabling real-time monitoring in freely moving animals, require costly surgical implantation, specialized proprietary hardware, and post-operative recovery periods that may confound early hemodynamic data. To address these limitations, this study presents a low-cost, customizable, and minimally invasive monitoring system utilizing a pressure transducer in the central auricular artery. The device integrates an ESP32 microcontroller with IoT technology for digital signal processing and seamless wireless data transmission to the ThingSpeak cloud platform. Unlike implantable telemetry, the proposed approach avoids surgical implantation and its associated costs and recovery time, while still enabling continuous, real-time hemodynamic tracking throughout the experimental period. A pilot evaluation against the BIOPAC MP100 reference (carotid artery) demonstrated relative errors of 1.60% for mean arterial pressure, 8.58% for systolic blood pressure, and 2.43% for diastolic blood pressure. By reducing invasiveness and enhancing remote data accessibility, this system provides a promising framework for the preclinical evaluation of antihypertensive agents and cardiovascular mechanisms, bridging the gap between edge computing and remote clinical diagnostics. Full article

This study analyzes how wind-induced vibrations can be exploited to harvest energy for powering remote weather stations. Three kinds of wind-induced vibrations are considered: vortex-induced vibrations, galloping, and flutter. Experimental tests on prototypes and numerical results show that the galloping harvester is the solution most suited to the proposed application. The numerical model makes it possible to simulate both T- and I-shaped harvesters and to analyze the effect of variations in the main design parameters: bluff-body mass, cantilever stiffness, and damping. Experimental tests show that a galloping energy harvester can supply an average power close to the average electrical load of an IoT wireless sensor for environmental monitoring, without requiring an additional battery supply. Full article

Mycotoxin contamination of grains during storage and transportation represents a significant threat to global food security. Conventional detection methods exhibit limitations in terms of real-time monitoring. This study presents a compact smart gas sensing system for mycotoxins, facilitating non-destructive testing of corn infected with fungi by analyzing the volatile organic compounds (VOCs) emitted during fungal growth. It also facilitates the precise quantitative detection of Aflatoxin B₁ (AFB₁). Additionally, a dual-branch convolutional neural network (DB-CNN) model has been developed to conduct an in-depth analysis of the temporal and spatial characteristics of VOCs signals. The system achieves 100% accuracy in identifying grains (corn, peanuts, wheat, and rice) infected with Fusarium graminearum and Aspergillus flavus by extracting the characteristic fingerprint spectra of fungal VOCs. In the quantitative analysis, the DB-CNN exhibits good performance (RMSE = 1.0292 μg/kg, R² = 0.9994). In addition, the designed detection system supports wireless transmission and can be connected to a smartphone for data transfer, thereby facilitating data storage and remote monitoring. The entire detection process is completed within 4 min. This study provides an innovative technical foundation for dynamic real-time monitoring of fungal contamination in the food supply chain, contributing to early warning systems and quality control measures. Full article

Advancement of precision agriculture increasingly relies on cost-effective and scalable technologies for real-time environmental management, particularly in greenhouse environments where vertical and spatial microclimate heterogeneity influences crop performance. This study presents the design, implementation, and experimental validation of an Android-based smartphone application edge supervisory monitoring system integrated with multi-layer wireless sensing and control nodes for real-time monitoring in a smart greenhouse. The system combined multi-layer wireless sensor nodes, wireless control nodes, a Long-Range Wide Area Network (LoRaWAN) gateway, Message Queuing Telemetry Transport (MQTT) communication, and a cloud-synchronized smartphone-based supervisory interface for visualizing environmental data, detecting defined abnormal events, and controlling actuators remotely. For feasibility tests, 54 sensing nodes and 12 actuator nodes were deployed across three vertical layers in two sections, measuring temperature, humidity, CO₂ concentration, and light intensity. Abnormality was defined as environmental threshold violations, statistical signal deviations, actuator power inconsistencies, and communication timeout events. Experimental results revealed vertical and spatial environmental variability across greenhouse sections, while real-time time-series and 3D spatial maps enabled the rapid detection of abnormal conditions. The rule-based abnormality detection engine identified out-of-range environmental values and sensor-related inconsistencies and generated immediate notifications. Smartphone profiling revealed that display and system-level processes accounted for energy consumption, with battery power reaching a peak of 3.5 W and application CPU utilization ranging from 40% to 70% during active monitoring. The results demonstrate system-level feasibility, responsiveness, and scalability under commercial greenhouse workloads, supporting future integration of predictive control and energy-efficient operation. Full article

Nowadays, thermal runaway accidents involving lithium batteries in new energy vehicles and energy storage power stations occur frequently, with battery deformation pressure as the core precursor signal. Traditional battery protection schemes suffer from limitations, including wired connections, limited real-time remote monitoring, and insufficient sensing accuracy, rendering them unable to meet the safety monitoring needs of large-scale battery modules. Therefore, a high-precision pressure-sensing battery protection system based on the Internet of Things has been developed. This paper selects a MEMS high-precision pressure sensor with an accuracy of ±0.1 kPa to design an IoT sensing node based on the STM32L431 and LoRa/Wi-Fi 6, integrating pressure sensing and wireless communication. It proposes a sliding-average filtering and wavelet denoising algorithm, as well as a temperature-compensation calibration model, to optimize sensing accuracy. Additionally, it constructs a hierarchical early warning model based on pressure thresholds. The experiment demonstrates that the sensor achieves a detection accuracy of 99.2%, a response delay of less than 50 ms, a transmission packet loss rate of less than 0.5%, an end-to-end delay of less than 200 ms, and an early warning accuracy rate of 99.2% under battery overcharge/overtemperature conditions. The innovation of this study lies in the first integration of high-precision pressure sensing and IoT communication for battery protection. A low-power IoT sensing node tailored for battery aging scenarios has been designed, validating the novel application value of IoT sensing in the safety monitoring of new energy equipment. This system fills a gap in IoT pressure-sensing technology for battery protection, enabling practical applications and serving as a reference for implementing integrated sensing and communication technology. Full article

The H2IOSC project aims to establish a federated cluster of European distributed research infrastructures involved in the humanities and cultural heritage sectors, with operating nodes across Italy. Through four key RIs—DARIAH-IT, CLARIN, OPERAS, and E-RIHS—the project promotes collaboration among researchers with interdisciplinary expertise. Within this framework, DIGILAB functions as the digital access platform for the Italian node of E-RIHS. Conceived as a socio-technical infrastructure for the Heritage Science community, DIGILAB is designed to manage heterogeneous data and metadata through advanced knowledge graph representations. The platform adheres to the FAIR principles and supports the complete data lifecycle, enabling the development and maintenance of Heritage Digital Twins. DIGILAB integrates diverse categories of information related to cultural sites and objects, encompassing historical and artistic datasets, diagnostic analyses, 3D models, and real-time monitoring data. This monitoring capability is achieved through the deployment of cutting-edge Internet of Things (IoT) technologies and large-scale Wireless Sensor Networks (WSNs). As part of DIGILAB, we developed SENNSE (v1.0), a fully open hardware/software platform dedicated to environmental and structural monitoring. SENNSE allows the remote, real-time observation and control of cultural heritage sites (collecting microclimatic parameters such as temperature, humidity, noise levels) and of cultural objects (collecting object-specific data including vibrations, light intensity, and ultraviolet radiation). The visualization and analytical tools integrated within SENNSE transform these datasets into actionable insights, thereby supporting advanced research and conservation strategies within the Cultural Heritage domain. In the following sections, we provide a detailed description of the SENNSE platform, outlining its hardware components and software modules, and discussing its benefits. Furthermore, we illustrate its application through two representative use cases: one conducted in a controlled laboratory environment and another implemented in a real-world heritage context, exemplified by the “Biblioteca Bernardini” in Lecce, Italy. Full article

Monitoring soil CO₂ is essential for accurately quantifying the sources and sinks of atmospheric greenhouse gases and for providing carbon emission reduction strategies. However, the limited portability and high cost of conventional soil CO₂ monitoring equipment have severely restricted large-scale and long-term field observations. To address these constraints, this study has successfully designed and fabricated a portable and low-cost soil respiration system (SRS) based on non-dispersive infrared (NDIR) sensor technology and Long-range radio (LoRa) wireless communication. The SRS enables multi-point synchronous measurements and remote data transmission. Its reliability was rigorously evaluated through both simulated and field comparative experiments against the LI-8100A. The results demonstrated a high level of agreement between the measurements of the SRS and the LI-8100A, with the coefficients of determination (R²) of 0.996 and 0.997, respectively, for the simulation and field experiments, with the corresponding root mean square error (RMSE) of 0.090 and 0.089 μmol·m⁻²·s⁻¹. The Bland–Altman analysis further confirmed the consistency between the two systems, with over 95% of the data points falling within the acceptable limits of agreement. These findings indicate that the self-developed SRS substantially reduces costs while maintaining reliable measurement accuracy. With its wireless transmission and multi-point deployment capabilities, the SRS offered an efficient and practical solution for addressing the challenges of monitoring spatial heterogeneity of soil respiration, demonstrating considerable potential for broader application in CO₂ flux monitoring research. Full article

This paper proposes a large-scale, self-healing multipoint fiber Bragg grating (FBG) sensor network that employs reinforcement learning (RL) techniques to enhance the resilience and efficiency of optical wireless communication networks. The system features a mesh-structured, self-healing ring-mesh architecture employing 2 × 2 optical switches, enabling robust multipoint sensing and fault tolerance in the event of one or more link failures. To further extend network coverage and support distributed deployment scenarios, free-space optical (FSO) links are integrated as wireless optical backhaul between central offices and remote monitoring sites, including structural health, renewable energy, and transportation systems. These FSO links offer high-speed, line-of-sight connections that complement physical fiber infrastructure, particularly in locations where cable deployment is impractical. Additionally, RL-based artificial intelligence (AI) techniques are employed to enable intelligent path selection, optimize routing, and enhance network reliability. Experimental results confirm that the RL-based approach effectively identifies optimal sensing paths among multiple routing options, both wired and wireless, resulting in reduced energy consumption, extended sensor network lifespan, and improved transmission delay. The proposed hybrid FSO–fiber self-healing sensor system demonstrates high survivability, scalability, and low routing path loss, making it a strong candidate for future services and mission-critical applications. Full article

Monitoring the health of power lines (PL) is essential for ensuring reliable power delivery, facilitating predictive maintenance, and maintaining a resilient grid infrastructure. Given the extensive length of PL networks, large numbers of wireless sensor nodes must be deployed, often in remote and harsh environments where battery replacement is costly and impractical. To address these limitations, this work proposes a hybrid energy-harvesting approach that combines piezoelectric and photovoltaic (PV) technologies to enable long-term, battery-free PL monitoring. The primary energy source is a compact, tunable, magnetically coupled piezoelectric vibrational energy harvester (VEH) that exploits local magnetic field distribution, inducing mechanical excitation of a cantilever and enabling the harvesting of vibrational energy near the PL at a frequency of 50 Hz. A complementary PV harvester is integrated to ensure operation during power outages or conditions where the piezoelectric excitation is reduced, thereby enhancing system robustness. Electromechanical characterization and a lumped-parameter model show good agreement with experimental results of the proposed VEH. The system is validated both on a PL test bench (5 A–10 A) and through inertial excitation using an electrodynamic shaker, demonstrating stable performance across a wide range of operating conditions. The combined hybrid architecture highlights a promising pathway toward self-sustaining, maintenance-free sensor nodes for next-generation power line monitoring. Finally, we demonstrate the feasibility of using such system for powering a WSN node by comparing the power produced by the proposed system with the power consumption of a potential application. Full article

Monitoring the curing process of concrete remains a challenging and critical aspect of modern construction, often hindered by labour-intensive, invasive, and inflexible methods. The primary aim of this study is to develop an integrated IoT-enabled platform for automated, real-time monitoring of concrete curing, using a combination of LoRa-based sensor networks and an LTE-M backhaul. The resulting ConMonity system employs embedded multi-sensor nodes—capable of measuring strain, temperature, and humidity–connected via an energy-efficient, TDMA-based LoRa wireless protocol to an LTE-M gateway with cloud-based management and analytics. By employing a robust architecture with battery-powered embedded nodes and adaptive firmware, ConMonity enables multi-modal, multi-site assessments and demonstrates stable, autonomous operation over multi-modal, multi-site assessment and demonstrates stable, autonomous operation over multi-month field deployments. Measured data are transmitted in a compact binary MQTT format, optimising cellular bandwidth and allowing secure, remote access via a dedicated mobile application. Operation in laboratory construction environments indicates that ConMonity outperforms conventional and earlier wireless monitoring systems in scalability and automation, delivering actionable real-time data and proactive alerts. The platform establishes a foundation for intelligent, scalable, and cost-effective monitoring of concrete curing, with future work focused on extending sensor modalities and enhancing resilience under diverse site conditions. Full article

Background: Wireless Vital Parameter Continuous Monitoring (WVPCM) allows the continuous tracking of patient physiological parameters, facilitating the earlier detection of clinical deterioration, especially in low-intensity care settings. The aim of this study is to evaluate the effectiveness of using WVPCM compared to the usual monitoring of critically ill patients hospitalized in Internal Medicine wards. An investigation of the attitude of health professionals towards the use of new technologies in daily practice to improve patient management was also carried out. Methods: The LIght Monitor Study (LIMS) is a prospective, open-label, randomized, multi-center pilot trial comparing WVPCM and conventional nurse monitoring during the first 72 h of hospitalization. A central randomization unit used computer-generated tables to allocate patients to two different types of monitoring. The main outcome was the occurrence of major complications. The study planned to enroll 296 critically ill patients with a Modified Early Warning Score (MEWS) ≥ 3 and/or National Early Warning Score (NEWS) ≥ 5 across two Internal Medicine (IM) Units in Italy. The investigation of the attitude of nurses towards the use of WVPCM was carried out by using a questionnaire and a qualitative survey. Results: Due to the COVID-19 outbreak, the study was interrupted early and only 135 patients (WVPCM = 68; standard care = 67) were randomized. One patient in the control group was excluded from analysis because of drop-out, leaving 134 patients for intention to treat analysis. No statistically significant differences between standard care and WVPCM were observed in terms of major complications (37.5%, vs. 31.2% p = 0.475), in-hospital mortality (17.5% vs. 11.1%, p = 0.309), and median hospital length of stay (9 vs. 10 days, p = 0.463). WVPCM decreased nursing workload compared to the control, as the average time spent by nurses on the detection of vital signs per patient was 0 min per patient per day compared to 24.4 min (p < 0.001) observed in the control group. Twenty-two percent of patients in the WVPCM group (15/68) experienced discomfort with the device, resulting in its removal. The investigation of nurses involved 16 out of 18 people participating in the study. Opinions on the wireless device for patient monitoring were particularly favorable; most of them considered remote monitoring clearly superior to traditional in-person visits and easy to use after a brief practice period. All participants recognized the safety benefits of the system. Conclusions: The reduced sample size of this pilot study does not allow us to draw any conclusions on the superiority of WVPCM compared to standard care in terms of clinical outcomes. However, we observed a positive trend in the reduction of major complications. Full article

To enable remote and automatic monitoring of the farmland soil information, this paper has developed a soil monitoring system based on the Internet of Things (IoT), which mainly involves the development of a gateway server node, wireless sensor nodes, a remote monitoring platform, and photovoltaic (PV) modules. The Raspberry Pi 5-based gateway server periodically sends data acquisition commands to wireless sensor nodes via LoRa, receives soil data returned by sensor nodes, and stores them in a MySQL database. Using a remote monitoring platform, Internet users can monitor real-time and historical soil data stored in the database. The STM32F103C8T6-based wireless sensor node receives data acquisition commands from the gateway server, uses soil temperature and humidity sensors as well as a pH sensor to collect soil status, and then sends sensor data back to the gateway server via LoRa. The system is powered by both PV energy and batteries, which enhances the endurance capability. Experimental results show that the designed system works well in remotely monitoring soil information. Using the proposed query attempt dynamic adjustment (QADA) method, the wireless sensor node dynamically adjusts the number of query attempts, which reduces the data acquisition failure rate from 21–25% to no more than 0.33%. Using the obtained qualitative relationship that the data acquisition delay varies inversely with the LoRa transfer rate, the data acquisition delay can be reduced to less than 67 ms. Full article

Background: Microsurgical breast reconstruction is advancing rapidly with the integration of innovative technologies that enhance surgical precision, safety, and outcomes. This narrative review highlights recent developments across four key phases: flap planning, flap harvest, microvascular anastomosis, and flap monitoring. Methods: To identify the most updated and relevant data, all content on «Aesthetic and Reconstructive Breast Surgery Network» (ARBS Network, Copyright 2025 Mark Allen Group, United Kingdom) was screened regarding new technology. The contributions were grouped into one of four key phases. More references related to the content viewed were then searched on the electronic database MEDLINE (Bethesda, MD: U.S. National Library of Medicine). Results: 24 contributions regarding new technology were identified on ARBS Network. Of these, 17 were relevant for this paper. Preoperative tools such as CT angiography and AI-based perforator mapping optimize surgical planning and execution. Robotic-assisted or endoscopic techniques for deep inferior epigastric perforator (DIEP) flap harvest enable minimally invasive dissection with reduced donor-site morbidity and improved muscle preservation. Robotic microsurgery, particularly with the MUSA and Symani^® Surgical System, allows for precise, tremor-free suturing of submillimeter vessels. Indocyanine green (ICG) angiography remains the gold standard for intraoperative perfusion evaluation. Postoperative flap surveillance is crucial for detecting vascular compromise early. Devices such as the Cook-Swartz Doppler probe and flow couplers offer continuous monitoring. Wireless oximetry systems like ViOptix^® provide non-invasive, real-time perfusion data and support remote monitoring. Conclusions: Collectively, these innovations are transforming microsurgical breast reconstruction by increasing efficiency, consistency, and outcomes. Full article

Underwater drones such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) are revolutionizing underwater operations and are essential for advanced marine applications like environmental monitoring, deep-sea exploration, and marine surveillance. In this paper, we concentrate on the enabling technologies and wireless communication strategies for underwater drones. Specifically, we analyze acoustic, optical, and radio frequency (RF) approaches, along with their respective advantages and disadvantages. We investigate the potential of integrating underwater drone-enabled wireless communication systems for smart marine communications. The study highlights the benefits of combining acoustic, optical, and RF methods to improve connectivity and data reliability. A hybrid underwater communication system is ideal for underwater drones because it can reduce latency, increase data throughput, and improve adaptability under various underwater conditions, supporting smart marine communications. The future direction involves developing hybrid communication frameworks that incorporate the Internet of Underwater Things (IoUT), AI-driven data, virtual reality (VR), and digital twin (DT) technologies, enabling a next-generation smart marine ecosystem. Full article

This research paper presents a smart IoT-based COVID-19 vaccine supply chain, monitoring, and control system. This proposed system is designed to efficiently and effectively monitor COVID-19 vaccine storage sites by tracking their temperature, humidity, quantity, and location on a map across various supply chain categories. It ultimately aims to monitor and control temperatures outside the range at the tracked location. The approach utilized temperature, humidity, and ultrasonic sensors, a GPS module, a Wi-Fi module, and an Arduino Uno microcontroller. The system was designed and implemented using Arduino and Proteus integrated design environments (IDEs) and coded using the embedded C/C++ programming language. A real-life working system prototype was designed and implemented. The measured sensor readings can be viewed via a computer system capability or any mobile device, such as an Android phone, iPhone, iPad, or laptop, with the aid of a cloud-based platform, namely, Thingspeak.com. The experimentally measured sensor readings are stored in a data log file for subsequent download and analysis whenever the need arises. The data aggregation and analytics are coded using MATLAB and viewed as charts, and the location map of vaccine carrier coordinates is sent to the web cloud for tracking. An alarm message is sent to the monitoring and control system if an unfavorable vaccine environment exists in either the store or the carrier container. A suitable sensor-based interface architecture and web portal are provided, allowing health practitioners to remotely monitor the vaccine supply chain system. This method encourages health workers by reducing the high levels of supervision required by vaccine supervisors to ensure the smooth supply of vaccines to vaccine collection centers, by using a wireless sensor network and IoT technology. Experimental results from the implemented system prototype demonstrated the benefits of the proposed approach and its possible real-life health monitoring applications. Full article