Search Results (701)

The continuous operation of the geophysical monitoring networks managed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV)–Osservatorio Vesuviano relies on the reliability of a highly distributed infrastructure deployed in active volcanic areas. In these contexts, failures affecting power supply, environmental control, or communication systems may lead to interruptions in data transmission and consequent loss of scientific observations. This work presents PROMETEO, an integrated remote-control and infrastructure monitoring system designed to supervise heterogeneous monitoring stations through a multiparametric sensing approach. The system combines distributed sensors and intelligent edge devices for the acquisition of electrical, environmental, and connectivity-related parameters, including battery voltage, load current, cabinet temperature, signal quality, and network reachability. Data are collected and integrated in real time through standard Internet of Things (IoT) and industrial communication protocols, namely Message Queuing Telemetry Transport (MQTT), Simple Network Management Protocol (SNMP), and MODBUS, and centralized within the open-source Home Assistant platform. This architecture enables the fusion of heterogeneous sensor measurements into a unified supervisory framework for real-time visualization, alarm generation, historical storage, and trend analysis. The results show that the multiparametric correlation of sensor data significantly improves diagnostic capability, allowing rapid discrimination between power-related anomalies and communication failures, particularly in remote mobile stations. By reducing diagnostic uncertainty and limiting unnecessary field interventions, PROMETEO enhances the operational resilience of geophysical monitoring infrastructures and supports preventive and predictive maintenance strategies. The proposed system demonstrates how a scalable multiparametric sensing architecture can strengthen the reliability and continuity of monitoring networks operating in complex environmental conditions. Full article

Liquid-nitrogen biobanks need temporary, spatially distributed temperature records, but conventional batteries and radios become unreliable far below their rated operating temperatures. We developed a finite-duration wireless logging system that separates sensing and communication in both space and time. A remote PT1000 probe follows the cryogenic environment, whereas a polytetrafluoroethylene (PTFE)/aerogel enclosure delays cooling of the battery and electronics; data are stored locally during exposure and retrieved by Bluetooth Low Energy only after warm-up. This differentiated thermal-path and staged-communication architecture is the principal novelty of this work. A transient node model reproduced the internal cooling trend, with a mean absolute error (MAE) of 3.74 °C, a root mean square error (RMSE) of 3.92 °C, and r = 0.9986, with a 123.5 s difference in the time to reach −50 °C. Nine nodes logged for 38.4–47.2 min (mean 44.2 min), and all reconnected after 35 min of warm-up at approximately 25 °C. In a one-node indoor engineering test, historical records were recovered without packet loss over 2–10 m, and 18 locations in an operating biobank yielded retrievable temperature histories. The system therefore supports short, non-real-time mapping and workflow assessment; it is not a substitute for fixed real-time alarm or metrological monitoring systems. Full article

On-skin non-invasive Wearable Health-Monitoring Devices (WHMDs) have rapidly evolved from laboratory prototypes into commercially viable systems capable of continuously tracking physiological and biochemical signals. By integrating epidermal temperature sensors, electrophysiological electrodes, biochemical sensing platforms, low-power electronics, and wireless communication technologies, these systems are emerging as key enablers of personalized and decentralized healthcare through the continuous acquisition of clinically relevant information directly from the skin surface. In this Perspective, we present our view on the state-of-the-art across the key technological pillars that define modern on-skin WHMDs, including non-invasive sensing strategies, advanced materials, processing and wireless communication units, energy-storage solutions, energy-harvesting techniques, and power-management architectures, with a particular focus on technologies that have already reached high Technology Readiness Levels (TRLs). We highlight how the next-generation of on-skin WHMDs must balance performance with sustainability and long-term reliability. This includes the adoption of biodegradable and recyclable materials, low-power and reconfigurable electronics, solid-state batteries, and hybrid energy-harvesting systems. By aligning technological innovation with human-centric and eco-friendly design principles, on-skin WHMDs can evolve into scalable, equitable, and environmentally responsible tools for future digital healthcare. Full article

Lithium-ion batteries undergo coupled thermal and mechanical responses during operation, which are closely related to their safety and reliability. However, simultaneously monitoring the surface temperature and strain fields of large-format prismatic batteries remains challenging. In this study, a fiber Bragg grating (FBG) array-based dual-parameter sensing system was developed to synchronously measure the temperature and strain of a 314 Ah lithium iron phosphate battery during charge–discharge cycling under different power conditions. Continuous surface temperature and strain fields were reconstructed from the measured data to investigate their spatiotemporal evolution. The results reveal significant differences between the temperature and strain distributions as well as asynchronous dynamic responses. Spatially, with increasing power, temperature hotspots shifted toward the central region, whereas strain extrema migrated toward the positive electrode side, revealing distinct spatial heterogeneity in the thermo-mechanical response. Temporally, the strain extrema consistently appeared tens to hundreds of seconds earlier than the temperature peaks. These findings provide direct experimental evidence of the thermo-mechanical coupling behavior of large-capacity lithium-ion batteries, establish baseline temperature–strain distributions under normal operating conditions, and offer valuable guidance for battery state evaluation, thermal management, and early fault diagnosis. Full article

The increasing demand for sustainable outdoor lighting has accelerated the development of solar-powered lighting systems. However, conventional solar lamps typically employ fixed illumination levels and simple day–night switching mechanisms, resulting in inefficient battery utilization and limited adaptability to changing environmental conditions. This study proposes a TRIZ-guided intelligent solar-powered lighting system that integrates photovoltaic energy harvesting, adaptive pulse-width modulation (PWM)-based illumination control, ultrasonic sensing, wireless communication, and embedded control into a unified standalone platform. The TRIZ contradiction matrix was employed during the conceptual design stage to systematically resolve key engineering contradictions involving illumination performance, energy efficiency, hardware complexity, battery lifetime, and user convenience. The proposed prototype was developed using an AT89S51 microcontroller to coordinate battery charging protection, environmental sensing, adaptive brightness regulation, and manual wireless operation. Experimental validation demonstrated stable photovoltaic charging with a regulated battery charging voltage of 14.4 V, reliable execution of embedded control functions, seamless transition between manual and autonomous operating modes, and adaptive LED brightness regulation according to real-time environmental conditions. The integrated PWM control strategy reduced unnecessary energy consumption by dynamically adjusting illumination intensity based on object detection rather than maintaining constant full-power operation. The experimental results further verified the feasibility of combining software-driven adaptive control with renewable energy harvesting to achieve intelligent energy management without increasing hardware complexity. Overall, the proposed system demonstrates that the integration of TRIZ-based systematic innovation with embedded intelligent control provides a practical, energy-efficient, and cost-effective solution for autonomous outdoor lighting. The proposed architecture offers valuable engineering insights for future smart lighting applications in off-grid infrastructure, sustainable communities, and smart city environments. Full article

Low-cost geared DC motors are widely used in mobile robotics and embedded mechatronic systems; however, their control remains challenging due to dead-zone nonlinearities, friction, low encoder resolution, motor asymmetry, and supply voltage variations. Biological organisms routinely perform motor control in the presence of similar uncertainties by relying on approximate reasoning and adaptive responses rather than precise mathematical models. This paper develops and experimentally validates a practical bio-inspired control architecture for low-cost geared DC motors operating under severe sensing and actuator limitations. The proposed controller combines fuzzy inference, dead-zone compensation, and a ramp-start mechanism to emulate the gradual and adaptive nature of biological motor responses. Instead of relying on an accurate plant model, control actions are generated through linguistic rules that mimic human-like decision-making based on speed error and error variation. The controller is implemented on an Arduino-based differential-drive robotic platform equipped with low-resolution optical encoders. Experimental results demonstrate that the proposed bio-inspired approach effectively mitigates startup stall, reduces oscillatory behavior caused by measurement quantization, and maintains stable speed regulation despite actuator variability and battery voltage fluctuations. The study shows that biologically inspired fuzzy control provides a practical and computationally efficient alternative to conventional PID methods for low-cost robotic systems characterized by significant uncertainty and nonlinear behavior. Full article

Visual Internet-of-Things (IoT) cameras and institution-controlled edge gateways increasingly collect artwork images in museums, galleries, and heritage sites. Centralizing these images can expose collection contents, exhibition layouts, and contextual information. This paper proposes FedArtSense, a privacy-preserving federated learning framework for artistic style and medium classification. FedArtSense combines discrepancy-adaptive dual-space prototype alignment, client-level differential privacy for model and prototype releases, and importance-aware shared sparsification compatible with secure aggregation. Experiments on WikiArt, ArtBench-10, and a seven-class Behance Artistic Media subset use emulated non-IID client partitions, persistent acquisition shifts, constrained uplinks, and client dropout. Under the default client-level target $(ϵ,\delta )=(6,{10}^{-5})$, FedArtSense obtains accuracies of $64.2\%$, $81.2\%$, and $75.3\%$, respectively, while reducing cumulative WikiArt uplink traffic to $11.5$ GiB. The results support FedArtSense as a privacy–utility–communication trade-off for gateway-assisted artistic image classification; retrieval, detection, aesthetic prediction, and direct battery-powered camera training are outside the evaluated scope. Full article

Continuous operation is increasingly expected of Internet of Things (IoT) and wireless sensor network (WSN) nodes, yet practical solar front ends must account for source variability, intermediate storage, conversion losses, sensing overhead, and battery-management constraints. This article develops and evaluates a discrete, two-stage photovoltaic front end for such nodes: a perturb-and-observe (P&O) buck stage tracks the maximum power point of a 20 W Solarland SLP020-12U module (rated 17.2 V, 1.16 A) and feeds an intermediate storage bus, while a PI-compensated SEPIC stage regulates the IoT rail to 3.2 V independently of that bus voltage. Closed-loop MATLAB/Simulink simulations are reported at 1000, 800, and 600 W/m². The reported conversion figures originate from an idealized switching model and should therefore be interpreted as simulation-only values rather than measured prototype efficiency. A low-cost Arduino-based prototype confirms correct switching behavior and a 20.0048 kHz PWM signal, but the available captures lack synchronized, calibrated input/output power logging; consequently, no hardware efficiency, MPPT tracking efficiency, regulation error, ripple, or settling-time figure is claimed. The revised manuscript makes this simulation-to-hardware boundary explicit, adds the power cost of sensing and data conversion to the loss discussion, strengthens the battery-management and deployment caveats, and defines the measurements required for full quantitative validation. Full article

Energy management in battery-powered embedded Artificial Intelligence of Things (AIoT) nodes requires coordinated control of sensing, computation, and wireless communication. This structured review combines a documented search of Scopus, Web of Science Core Collection, and IEEE Xplore with screening of 548 unique records and full-text source-to-construct coding of 56 database-derived sources. The synthesis shows that acquisition, Tiny Machine Learning (TinyML) inference, communication, protocol-state overhead, adaptive sensing, and energy harvesting are commonly optimized separately, while field-validated joint control remains limited. The review develops a full-cycle energy model and cross-layer framework linking sensing, processing, payload representation, radio policy, and energy availability. Dimensionless break-even analysis shows that the admissible local-processing budget is bounded by fixed communication overhead and non-machine-learning edge costs. A targeted Long-Term Evolution for Machines (LTE-M) experiment comprising 40 complete transactions showed that payload length alone was insufficient to characterize communication energy; a state-aware model incorporating measured signaling duration achieved $R^2=0.980$ and predicted a temporally separated second session with 1.66% relative root mean square error (RMSE). Precision agriculture and precision apiculture illustrate modality- and network-dependent trade-offs. The findings support full-cycle benchmarking and context-aware orchestration rather than isolated optimization of inference or communication. Full article

Smart textile interfaces hold promise for battery-free wearable interaction, yet their adoption is limited by complex fabrication and insufficient on-body evaluation. We present TriboTex, a low-cost spray-patterning workflow that forms nylon–Cu–nylon triboelectric stacks on cotton textiles using laser-cut PET stencils and commercially available materials. The core consumables cost approximately USD 0.003/cm², and sensor geometry can be rapidly iterated by modifying only the digital stencil. Controlled characterization across nine devices from three fabrication batches showed a peak open-circuit voltage of 52.3 V and a maximum power density of 1870 µW/m² at 4 GΩ. The output retained 96.1% of its initial voltage after 1000 bending cycles and 94.2% after 24 h of simplified saline immersion. Three-sample environmental sweeps showed voltage amplitudes of 41.9–43.7 V from 15 to 45 °C, with a decrease to 27.7 V at 0 °C; the humidity response remained within 92.7–104.5% of the 20% RH value over 20–60% RH but decreased to 19.9% at 70% RH. Two wearable prototypes were developed: a single-electrode garment sleeve recognized tap, double-tap, and swipe gestures with 95.0% accuracy across 1200 trials from 12 participants; a single-electrode insole generated action-dependent peak voltages up to 123 V under repeated foot loading and was connected through a rectification and voltage-regulation module to charge a battery. Across the two 12-participant studies, attachment and fit stability emerged as shared integration requirements, while participant feedback and controlled humidity measurements highlighted moisture management as a priority for reliable on-body sensing and energy capture. The primary contribution is an accessible, low-cost, and geometry-flexible route for early-stage wearable sensing experiments and application demonstrations, supported by documented fabrication, electrical characterization, and human-centered evaluation. Full article

Conventional forest inventories provide standardized but temporally discrete DBH observations, whereas some research and management applications require continuous observations of diameter dynamics between remeasurement campaigns. This study designed and implemented a Smart Tree Monitoring Network based on Internet of Things (IoT) and cloud storage technologies as a complementary intensive-monitoring approach for selected forest plots. The system enables automatic, continuous, networked observation of standing-tree diameter growth and consists of Tree Sensor Nodes (TSNs), Stand Gateways (SGs), and a cloud management platform. The independently designed tree diameter growth monitoring instrument senses micro-variations in DBH and conducts scheduled data acquisition. Low-power wireless transmission from TSNs to gateways is achieved through LoRa/LoRaWAN, while stand gateways aggregate multi-node data and environmental parameters and upload them to the cloud platform via a 4G network. Field deployment involved 426 devices in 10 sample plots with different terrain and climatic conditions in Hubei Province. The results showed that (1) with a 3.6 V, 19,000 mAh lithium battery and a 5 min sampling interval, daily power consumption was 5.37 mAh, corresponding to a theoretical battery-life estimate of 9.69 years under the tested duty-cycle assumptions; (2) at initial deployment, device-measured DBH showed strong agreement with manual measurements, with R² = 0.9996, RMSE = 0.215 cm, MAE = 0.170 cm, and Bias = −0.089 cm, while subgroup analyses indicated larger underestimation for large-diameter trees; and (3) monthly mean RSSI and SNR remained above the adopted reference thresholds throughout 2025, while rainfall and temperature were associated with limited variation in signal quality. These results support the technical feasibility of the system for high-frequency DBH monitoring in selected plots, while long-term measurement drift, end-to-end data completeness, battery life under field aging, and physical durability require further validation. Full article

Wireless sensing inside rotating industrial machines is challenging due to signal attenuation, multipath propagation, and continuous sensor motion. Smart particles equipped with wireless communication and inertial sensors provide a promising approach for monitoring such systems. However, the reliability of wireless signal transmission under rotational dynamics remains insufficiently understood, and systematic approaches for sensor selection are lacking. This paper presents a link-budget-normalised experimental characterisation of three commercial smart particles, namely MetaMotionS (BLE), WitMotion BLE, and WitMotion Wi-Fi, in a bare 300 mm diameter by 310 mm deep metallic drum fitted with six triangular lifters, at rest and at 16, 18, and 20 RPM, corresponding to 20.7–25.9% of the critical speed and Froude numbers of 0.043–0.067. Because raw received power conflates transmit power with channel behaviour, the comparison is expressed as excess path loss above free space together with second-order fading statistics. Two particles of the same protocol class differ by 23.9 dB, of which at most 4 dB is attributable to the transmission of power across the documented range of both radios, establishing that device implementation rather than protocol class governs the ranking. Two particles logged simultaneously through a single receiver observe one channel realisation, and the correlation between their signal fluctuations is not significantly different from zero at any speed (r = +0.064, −0.098, −0.083), indicating device-specific rather than environmental fading. Under rotation, the Wi-Fi particle holds an RSSI standard deviation of 1.96 dB against 6.27 and 6.58 dB for the two BLE particles (Welch ANOVA, p < 0.001; Games–Howell post hoc, all pairwise comparisons p < 0.001; |Cliff’s δ| > 0.96). A bounded, dimensionless multi-criteria selection procedure over link margin, signal variability, cross-speed consistency, packet delivery, and energy per delivered packet is introduced; the ranking is invariant under weighted-sum and TOPSIS aggregation but inverts once endurance carries a weight above 0.35, which quantifies the trade-off between link quality and battery life. Coupling between the wireless and motion streams is examined by folding both onto rotation phase. A rotation-locked component in RSSI is detected in one of nine sensor–speed combinations, with a maximum modulation amplitude of 0.92 dB. The RSSI logging cadence of approximately 1 Hz resolves the drum fundamental but lies below the Nyquist requirement for the dominant motion band at 0.91–1.04 Hz, so joint wireless–motion studies of rotating machinery require RSSI logging at 5 Hz or above on a clock shared with the inertial unit. Full article

Electrochemical impedance spectroscopy (EIS) resolves the internal physicochemical processes of lithium-ion batteries across timescales—from ohmic conduction through charge-transfer kinetics to solid-state diffusion. Despite this analytical power, EIS deployment remains largely confined to laboratory electrochemical workstations that are bulky, expensive, and incapable of online multi-cell operation under dynamic DC bias conditions. This study presents a multi-channel EIS measurement device that simultaneously addresses three requirements for practical battery diagnostics: workstation-grade measurement accuracy, multi-cell synchronous acquisition, and tolerance to the DC bias voltage present across battery terminals during operation. The device employs a master–slave distributed architecture: each slave unit is built around the DNB1101 battery-dedicated impedance measurement chip with a Kelvin four-wire sensing configuration, while the STM32F407-based master controller coordinates measurement scheduling and data communication under FreeRTOS. A four-channel slave board with a differential daisy-chain communication topology and hardware broadcast trigger mechanism supports multi-cell synchronous acquisition. The device operates over a frequency range of 0.01 Hz to 5620 Hz with logarithmic spacing, and a C#-based host application provides real-time Nyquist and Bode visualization along with MATLAB R2024a-based post-processing for outlier rejection and data smoothing. Validation was conducted using Panasonic NCR18650 ternary (NCA) and LiFePO₄ (LFP) 18650 cells, benchmarked against a CorrTest CS350 electrochemical workstation at SOC = 40% and 25 °C. The device achieves a maximum impedance magnitude error of 1.55% and a maximum phase error of 1.22%. Equivalent circuit model fitting via ZSimpWin yields parameter differences below 1% between the device and the reference workstation. Under online conditions with a 3.6 V DC bias, the impedance measurement deviation of a 20 mΩ precision resistor remains below 0.69% across the full frequency range. Multi-channel synchronous measurements across four cells demonstrate inter-channel amplitude variance below 2.13%. Cross-chemistry validation with LiFePO₄ cells yields magnitude and phase errors below 0.92%. These results demonstrate that the proposed device provides laboratory-grade EIS accuracy with multi-channel, online, and cross-chemistry capabilities, offering a practical platform for integrating EIS-based diagnostics into next-generation battery management systems. Full article

Road traffic surveys require sensing systems that can be deployed rapidly without modifying the road surface or requiring a permanent power connection. This paper presents the design, embedded implementation, and evaluation of a low-power roadside magnetic sensor that performs vehicle-event detection and classification directly at the edge. The sensing node integrates two RM3100 three-axis magnetometers (PNI Sensor, Santa Rosa, CA, USA) with an NXP MK22FN512VLH12 microcontroller (NXP Semiconductors N.V., Eindhoven, The Netherlands) based on a 120 MHz Arm Cortex-M4F core with 512 kB Flash and 128 kB SRAM. Magnetic-field data are acquired at 250 Hz and processed locally using baseline removal, low-pass filtering, signal-energy calculation, and peak-based event detection. Detected magnetic signatures are classified using an integer-quantised one-dimensional convolutional neural network implemented directly on the microcontroller. The model processes four synchronised 512-sample channels representing the three magnetic-field axes and their combined signal energy. Model development was supported by approximately 50,000 annotated events obtained from 36 h of real-world traffic measurements at eight locations. The selected model achieved an overall classification accuracy of 91.1% for the considered operational categories. The implemented network requires 288,128 multiply–accumulate operations per inference, while its quantised weights and biases occupy approximately 23 kB of Flash memory. Complete three-axis event signatures are stored locally for subsequent verification, whereas only the timestamp and predicted vehicle category are transmitted through the wireless interface. Based on the capacity of the applied LiFePO₄ battery and the estimated consumption of the implemented hardware, the expected autonomous operating period is approximately 41 days. The results demonstrate the feasibility of integrating magnetic sensing, embedded signal processing, and Edge AI on a conventional resource-constrained Cortex-M4 platform for non-invasive road traffic monitoring. Full article

This article describes a new hardware/software alert system designed to assist elderly people and prevent falls due to bed-exit events at night. To assist the elderly during the night, it is important to implement automatic lighting activation in the bedroom. This helps reduce the risk of falls when they need to, for example, go to the bathroom. The caregiver can be alerted immediately via Wi-Fi, during the night, providing immediate assistance to the elderly person. The developed HW/SW system combines a passive infrared motion sensing device, a light sensor, and dedicated hardware based on the ESP32-C6 RISC-V microcontroller that communicates via Wi-Fi with a developed Android dedicated App, which the caregiver can access using a tablet or smartphone. Nighttime falls remain one of the most serious health problems for older people. The main innovative contribution of this work is the development of a low-cost preventive battery-free assistive system that does not require an internet access contract, preserves the elderly person’s privacy, and promptly alerts the caregiver whenever the elderly person gets out of bed during the night. The system is directly integrated with automated lighting, preventing the elderly person from walking in the dark and without appropriate aid. The system also supports the caregiver by generating alerts through an open-source mobile App. Full article