Photoacoustic gas sensors are gaining increasing importance for environmental and industrial applications due to their high sensitivity and selectivity. However, their performance, when resonator measurement cell is used, is often degraded by acoustic noise interference and constraints related to energy efficiency. This work presents a novel and straightforward approach for noise suppression based on the excitation of higher-order acoustic resonant modes, without altering the geometry or structure of the resonant setup. Specifically, we experimentally demonstrate the benefits of operating at the third acoustic resonance $(\sim 12 \text{kHz})$ instead of the fundamental mode $(\sim \mathbf{4 ~ k H z})$ for the cell under test. This frequency shift moves the system operation into a spectral region less affected by ambient disturbances, leading to a significant improvement in measurement stability and signal-tonoise ratio. Finite element method simulations and experimental validation confirm up to $\mathbf{1 5 ~ d B}$ increase in SNR and a fifty-fold reduction in average power consumption under pulsed operation, while maintaining equivalent gas detection sensitivity. The results show that the third-mode operation preserves the acoustic coupling efficiency despite a lower Qfactor, and it ensures stable performance even under noisy laboratory conditions $(\sim 70 \text{dBA})$. The proposed strategy thus provides an effective, low-cost, and energy-efficient solution for enhancing robustness and reliability in photoacoustic gas sensors, paving the way for their integration into compact, autonomous, and distributed sensing platforms.

In this work we investigate the behavior of a toroidal photoacoustic resonator to provide compact, physics-guided analytical relationships that link its geometry to two key parameters: resonance frequency and quality factor. Finite-element data are combined with reduced-order analytical models to refine a corrected toroidal-resonance frequency model that accounts for effective propagation length and thermo-viscous effects. For the quality factor, a simple law motivated by a boundary-layer dissipation model is proposed. Derived models are validated by experimental tests performed using three 3D printed toroidal resonators in different sizes. Experimental results confirm the prediction both for the first and third resonance frequencies with an average relative error below 1%, outperforming cylindrical and uncorrected baseline models available in the literature. The results also confirm the predicted trend of the quality factor with respect to the torus’s minor radius, highlighting a direct relationship between the cross-sectional area and acoustic losses, which governs the balance between stored acoustic energy and thermo-viscous dissipation. Overall, the framework provides quick, interpretable design rules that reduce dependence on extensive finite-element method simulation campaigns for first-pass estimation of resonant behavior during the early design phase and guiding the optimization of high-performance PAS devices while preserving accuracy.

This paper proposes a reconstruction methodology based on a one-dimensional Convolutional Denoising Autoencoder (1D-CDAE) for recovering missing air temperature values in agricultural IoT time-series data affected by extended transmission losses. The approach is assessed using real-world microclimatic measurements acquired from a wireless sensing node operating within a vineyard monitoring system. Both single-variable and multivariate reconstruction schemes are investigated. While the single-variable formulation relies exclusively on historical temperature data, the multivariate approach leverages relative humidity through a dual-encoder architecture combined with a cross-attention fusion mechanism, allowing the selective exploitation of physically correlated environmental information to enhance temperature reconstruction, particularly under extended missing intervals. Experimental results on real vineyard data show that the proposed multivariate 1D-CDAE consistently outperforms the temperature-only configuration, with particularly significant accuracy improvements for long missing-data segments, highlighting its effectiveness as a robust preprocessing tool for agricultural IoT decision-support applications.

This article presents the design, implementation, and validation of a flexible and scalable Internet of Things (IoT) measurement platform specifically conceived for environmental and agricultural monitoring applications. The system integrates heterogeneous sensors, multiradio connectivity, and solar energy harvesting for long-term autonomy. A one-year deployment in a vineyard near Siena, Italy, confirmed high data consistency, energy autonomy, and network robustness. The long-range wide-area network (LoRaWAN) achieved a packet loss below 2%, and the power management unit ensured continuous operation without unexpected outages. Cross-validation among sensors and reference instruments confirmed the stability of the measurements, with negligible drift, and the consistent correlation between the measured quantities was proved.

Chemoresistive gas sensors based on metal oxides are widely used in environmental monitoring, industrial safety, and smart systems. Despite their commercial relevance, the underlying physico-chemical processes that govern their electrical response remain partially understood, limiting the development of robust, drift-resistant, and adaptive sensor systems. This paper investigates the potential of numerical modeling as a computational approach to describe and predict the dynamic behavior of these sensors under varying gas compositions and thermal conditions. We present the surface-state–based modeling framework that integrates the kinetics of surface reactions with electronic conduction mechanisms, focusing on n-type metal oxide materials. By translating the complex interaction between gas species and the sensing material into a system of equations, the model enables simulation of the transient and steady-state conductance. This approach provides not only a compact description of sensor behavior but also a foundation for algorithmic learning and control strategies in complex environments. Our findings highlight how such numerical models can serve as interpretable algorithmic abstractions, bridging physics-based sensor modeling with data-driven inference in artificial intelligence systems.

This work presents a compact, self-powered wireless CO2 sensing node for autonomous environmental monitoring. The system integrates a high-efficiency multijunction photovoltaic (PV) module, a 4000 F hybrid supercapacitor operating at 3.6–4.2 V, and a custom power management system in a LiPo-sized form factor. The PV module, composed of nine parallel triple-junction solar cells, achieves an average efficiency of 27% and delivers peak power at 4.26 V under 600 W/m2 irradiance. The sensing unit includes miniaturized CO2, humidity, and temperature sensors with LoRa-based wireless communication. The low-power NDIR CO2 sensor provides a resolution of 15–20 ppm and a response time of ~45 s. Week-long tests demonstrated fully autonomous operation with reliable 5 min data transmission, capturing diurnal CO2 variations associated with plant activity even under low irradiance. Energy storage occurs for irradiance levels ≥65 W/m2, and long-term simulations confirm stable supercapacitor voltage over yearly cycles. This work demonstrates a compact multijunction solar–hybrid supercapacitor platform capable of sustaining WSN for long-term, maintenance-free CO2 monitoring under real-world and low-irradiance conditions. Our results demonstrate that the sensing node can reliably monitor plant-driven CO2 dynamics, clearly resolving the expected photosynthesis–respiration cycles and their dependence on incident solar radiation, while simultaneously sustaining its energy budget under highly challenging illumination and transmission conditions.

Multi-heater metal-oxide (MOX) gas sensors are widely used in low-cost IoT nodes, as temperature-cycled operation is often assumed to increase informational content. However, the effective dimensionality of multi-heater measurements under real conditions is still not well quantified. This work presents a measurement-oriented assessment of a commercial multi-heater MOX sensor (BME688) deployed in a network of low-power IoT nodes and operated in realistic indoor and outdoor environments. Eight co-located nodes were monitored for about four months to evaluate inter-device consistency. Channel-to-channel correlations and principal component analysis (PCA) were used to analyze the multichannel responses. Results show strong temporal coherence across nodes and high collinearity among heater steps. The first principal component explains more than 99% of the variance in typical indoor conditions, indicating that the nominal number of heater steps overestimates the effective dimensionality. These results highlight that the informational benefit of multi-temperature operation is strongly scenario- and time-scale-dependent, calling for a measurement-grounded use of multi-heater MOX sensors in IoT monitoring applications.

This work presents a low-cost quartz crystal microbalance with dissipation (QCM-D) monitoring system implemented on a field programmable gate array (FPGA) platform. The design optimizes the tradeoff between measurement accuracy and system complexity by improving the analog front-end, signal acquisition, and digital signal processing stages. A short-circuit current readout configuration minimizes the effects of parasitic capacitances and load resistance, ensuring that the measured transient depends only on the electromechanical behavior of the resonator. The system employs a direct digital synthesizer (DDS)-driven mixing stage for downconversion to baseband, followed by adaptive processing on the FPGA, which extracts the resonance frequency and dissipation factor with high precision. In addition to the system implementation, this article includes an analysis of the main measurement errors and uncertainty sources, providing a systematic framework to assess the overall metrological performance of this type of architecture. Experimental results demonstrate frequency errors in the parts-per-million (ppm) range and dissipation errors on the order of $10<^>{3}$ , confirming that accurate and repeatable QCM-D measurements can be achieved using compact and cost-effective FPGA-based instrumentation suitable for both laboratory and portable applications.

Low-cost MEMS accelerometers are increasingly used for vibration-based condition monitoring, but their high-frequency performance is often limited by the way they are mechanically mounted. In many practical implementations, the coupling between the sensor and the monitored structure is poorly controlled or only marginally addressed, leading to mounting-induced resonances that can compromise broadband measurements. This paper presents a low-cost wideband vibration sensing system based on a MEMS accelerometer and an epoxy-encapsulated mounting solution specifically designed to improve mechanical coupling and shift parasitic resonances toward higher frequencies. Unlike approaches that mainly focus on signal processing, the proposed system experimentally addresses the mechanical integration of the sensor as a key requirement for reliable vibration measurements. The sensing unit is combined with embedded spectral feature extraction, which reduces data dimensionality by retaining the most relevant frequency components and provides compact feature vectors suitable for Artificial Intelligence (AI) and Machine Learning (ML) based condition monitoring. Finite element simulations, laboratory frequency-response tests, and automotive measurements on different engine configurations show that the proposed approach improves the dynamic behavior of the sensor and captures distinctive vibration signatures for automotive and industrial diagnostic applications.

In this work the feasibility of a high responsive humidity sensor realized through a quartz crystal microbalance functionalized by nanolayers of indium oxide (In2O3) is proposed. The sensing material is deposited by magnetron sputtering method and manufactured in oxygen-free atmosphere, thus named In2O3-x. The humidity-sensing performance of the functionalized quartz sensor was investigated across various humidity levels by monitoring shifts in the quartz resonance frequency induced by absorbed water molecules on the sensing film surface. Appreciable results are observed concerning the humidity sensor response, that follows a linear pattern for humidity levels below approximately 50

This paper presents the reliability assessment of an IoT-based sensor node designed for detecting combustible gas leaks in residential environments. Building on a previously published design that integrates low-power micromachined (Micro-Electro-Mechanical Systems, MEMS) pellistors and electrochemical Volatile Organic Compounds (VOC) sensors, this study evaluates the node’s long-term robustness and stability under both realistic and accelerated operating conditions. The system employs a dual-sensor strategy in which the VOC sensor acts as a sentinel, activating the pellistor only when necessary, thereby optimizing power consumption and extending battery life. BLE and LoRa communication capabilities support flexible deployment and real-time data transmission. To ensure suitability for safety-critical applications, we conducted comprehensive reliability testing, including accelerated life tests and environmental stress testing in compliance with IEC 60068 standards. The results confirm the system’s ability to maintain consistent performance and data integrity under thermal, mechanical, and chemical stress, demonstrating its robustness for prolonged operation in demanding environments. Overall, this work underscores the importance of rigorous reliability validation for IoT-based safety devices and positions the proposed solution as a significant step toward enhancing residential gas safety, with potential applications in broader industrial monitoring scenarios.

This study presents a flexible Internet of Things (IoT) monitoring system suitable for gas sensing, applicable to the pervasive experimental assessments of CO2 concentrations, particularly in relation to plant respiration and chlorophyll photosynthesis. Beyond CO2 measurement, the system incorporates the monitoring of other parameters meaningful for photosynthesis and possible plants stress factors, such as solar radiation intensity, temperature and water vapor concentration. Concerning the solar radiation monitoring, two low-cost sensing techniques are proposed based on the measurement of the short circuit current of a monocrystalline silicon photovoltaic (PV) panel and on CdS light dependent resistors (LDRs). A preliminary experimental campaign for the measurement of global and diffuse radiation is performed outdoors and the outcomes are compared with the acquisitions of a commercial pyranometer. Then, tests are performed in the laboratory with an experimental setup envisaging a closed accumulation chamber housing some plants exposed to diffuse radiation, with the final goal of monitoring the CO2, temperature and water vapor changes inside the chamber and of providing a rough assessment of the mean daily CO2 uptake of the plants under test.

In this paper, the authors propose a novel sensing technique for Structural Health Monitoring (SHM) based on the use of an array of magnetic field sensors to detect three-dimensional displacements of structural cracks, particularly in historical buildings. The proposed system employs at least three magnetic field sensors to measure variations in the magnetic field generated by a permanent magnet, enabling the detection of crack movements in all three spatial directions (x, y, z) exploiting the signals of three sensors. The sensor array is fixed on one side of the crack, while the permanent magnet is positioned on the opposite side using a cantilever-like structure. This configuration translates each crack movement into distinct variations in the sensor outputs, allowing for the continuous and real-time monitoring of the magnet's relative motion with respect to a reference position, with sufficient accuracy and low power consumption. The effectiveness of the proposed solution was validated through laboratory experiments over a displacement range of 10 mm along three orthogonal axes, demonstrating its potential to effectively track displacements associated with crack evolution. This provides a foundation for implementing non-invasive, low-cost distributed sensor networks in SHM applications.

We present a methodology to design a new class of low-complexity entropy estimators, aimed at designing tunable True Random Number Generators (TRNGs). Our design approach is detailed for the typical scenario of non-IID ergodic sources, analyzing how source memory impacts estimation precision. Additionally, we have examined the refined hardware implementation of this estimator, specifically for a Xilinx Artix 7 FPGA.

In this work, the feasibility of a sensing device to be employed for the real-time measurement of ferromagnetic contaminants in water is shown. Ferromagnetic materials, characterized by a high magnetic permeability, belong to the broader family of heavy metals, whose contribution in water pollution is well known. To measure their presence in water, a novel prototype of probe structure is designed and implemented. The probe integrates a tunneling magnetoresistance (TMR) sensor and a permanent magnet positioned at a fixed distance: the presence of the ferromagnetic materials in water is detected by the probe once it is inserted in water by measuring the variation in the magnetic field generated by the permanent magnet by means of the TMR sensors. The voltage output of the TMR sensor is then acquired and made available through an ad hoc conditioning electronic and a microcontroller unit (MCU). To demonstrate the effectiveness of the proposed approach, tests were performed in the laboratory using ultrapure (UP) water, in which increasing quantities of the three ferromagnetic elements present in nature (i.e., iron, cobalt, and nickel) are added. The results of the tests demonstrate a linear relationship among the ferromagnetic materials concentrations in water and the TMR sensor output, demonstrating the feasibility of the proposed approach and the usability of the designed probe also for real-time, in situ measurements.

This paper explores the principles and advantages of utilizing a pulsed measurement mode to enhance the energy efficiency of photoacoustic (PA) gas sensing systems configured with an acoustically resonant measurement setup. The proposed approach involves the intermittent excitation of one of the resonator's eigenmodes, which serves as the PA measurement cell (PA cell). By leveraging the resonant characteristics of the PA cell, the method maximizes sensitivity and signal-to-noise ratio (SNR) while significantly reducing power consumption compared to conventional continuous-excitation PA gas sensors. Experimental results demonstrate that with a peak current of less than 100 mA at 3.5 V for a 300 ms excitation pulse, a sensitivity of 9 mV/ppm can be achieved. Furthermore, by adjusting the duty cycle and the on-time duration, the system allows for flexible tuning of both power consumption and resolution, making it suitable for a wide range of applications. The study also highlights the importance of accounting for the dynamic characteristics of the system, such as settling times and signal delays, to avoid operating during transient states that could lead to incomplete or distorted measurements. This balance between energy efficiency and stable, steady-state operation is critical, particularly in applications where both precision and responsiveness are required.

Soil impedance monitoring is crucial for various applications, but traditional impedance analyzers, while capable of providing detailed frequency-dependent data, are costly and difficult to integrate into low-cost monitoring systems. Moreover, interpreting soil impedance remains challenging due to the heterogeneous nature of soil and the variability of its electrical properties. This paper presents a simplified, low-cost, low-complexity soil monitoring interface mainly based on internal MCU peripherals and minimizing the usage of external circuitry. GPIO ports are used to generate low-frequency square wave driving signals while internal pull-up and pull-down resistors or external fixed resistors are used to measure soil impedance across different resistance ranges. The system minimizes polarization effects on the sensor electrodes, enabling stable measurements over time. The system was tested with resistive loads in the (100 Omega, 200 k Omega) range and a measurement error lower than 2% was achieved. Then, tests with soil samples at different volumetric water contents were performed, proving the system effectiveness for real-time monitoring and IoT integration. Finally, the front end was integrated into an IoT monitoring system for smart agriculture, demonstrating its usability over long measurement periods.

Piezoelectric crystals, essential in modern electronics, function as precise electromechanical resonators and are used as sensors known as Quartz Crystal Microbalances (QCMs). These sensors detect interactions with surrounding media, altering their resonant characteristics. QCMs are versatile, measuring parameters like mass, viscosity, temperature, and humidity, and are used in various applications, including gas detection and biosensing applications. Measurement techniques for QCMs include impedance analysis, electronic oscillator-based techniques, phase shift measurements, and transient response analysis. Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) stands out for tracking resonance frequency and quality factor simultaneously, providing insights into mass and viscoelastic properties. This paper presents a method for extracting these parameters with high resolution and minimal computational effort, focusing on the dissipation factor, extracted with a novel processing technique starting from the transient signal amplitude. Using a 10 MHz AT-CUT quartz crystal, the method performance is evaluated under different mechanical loads, with signal processing techniques applied in the frequency domain to extract key parameters.

Low-cost miniaturized gas sensors are increasingly considered for outdoor air quality monitoring, yet their performance under real-world environmental conditions remains insufficiently characterized. This work evaluates the dynamic gas response of the Bosch BME688 sensor, whose metal oxide sensing layer is based on tin dioxide (SnO2) material, focusing on its sensitivity, selectivity, and dynamic response to four representative air pollutants: nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and isobutylene. This study provides both quantitative performance metrics and a physicochemical interpretation of the sensing mechanism. Controlled experiments were conducted in a custom test chamber to facilitate the precise regulation of temperature, humidity, and gas concentrations in the ppm to sub-ppm range. Despite large variability in the baseline resistance across devices, normalization yields consistent behavior, enabling cross-sensor comparability. The results show that the optimum operating temperatures fall in the range of 360–400 °C, where response and recovery times are reduced to a few minutes, compatible with mobile sensing requirements. Moreover, humidity strongly influences sensor behavior: it generally decreases sensitivity but improves kinetics, and in the case of CO, it enables enhanced responses through additional hydroxyl-mediated pathways. These findings confirm the feasibility of deploying BME688 sensors in distributed outdoor monitoring platforms, provided that humidity and temperature effects are properly addressed through calibration or compensation strategies. In addition, the variability observed in baseline resistance highlights the need for normalization and, consequently, individual calibration steps for each sensor under reference conditions in order to ensure cross-sensor comparability. The findings provided in this study provide support for the design of robust, low-cost air monitoring networks.

This paper investigates the effect of read-out electronics on the current measurements of a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D). The study focuses on how the design of the front-end circuit, particularly parasitic capacitances and load resistance, influences the accuracy of the quartz current measurements. Current-based measurements are shown to be robust, with design choices being less critical than voltage-based ones. Experimental results support the theoretical analysis, providing guidelines for improving the reliability and accuracy of current-based QCM- D measurements, maintaining resonance frequency errors in the range of parts per million (ppm) and dissipation (time constant) errors in the range of 10-3 in different measurement conditions.