Continuous monitoring of climatic variables is essential for precision viticulture and data-driven decision support systems. However, agricultural sensor networks are frequently affected by missing data due to hardware failures, communication issues, or maintenance interruptions. In this work, we propose a spatio-temporal graph-based autoencoder for reconstructing missing temperature and relative humidity time series collected from a five-node vineyard sensor network over a two-year period. The model combines a GRU-based temporal encoder, augmented with a time-decay imputation mechanism applied to the input data, with a GraphSAGE spatial module, enabling the joint exploitation of temporal dynamics and inter-node spatial correlations. Experimental results on real-world data show that the proposed approach achieves accurate reconstruction under controlled missing-data scenarios generated through structured artificial masking. For moderate corruption levels (p=0.3), the model attains reconstruction losses of 0.003 for temperature and 0.005 for humidity using short temporal windows (L = 36~3 h), corresponding to MAE values below 0.03 °C and 0.1%, respectively. Even at higher corruption levels (p=0.7), performance remains stable, with losses below 0.008 and 0.011, and MAE values within 0.05 °C and 0.17%. The results highlight a trade-off between temporal context and reconstruction accuracy: shorter windows yield lower absolute errors under moderate corruption whereas, under extreme data loss (p=0.9), the longer windows (L = 144~12 h) reduce the composite temperature reconstruction loss from 0.027 to 0.021. Additionally, temperature is consistently reconstructed more accurately than humidity, reflecting its smoother dynamics and stronger spatial coherence.

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 work presents a systematic analysis of power consumption and power-management strategies for a magnetic probe designed to detect ferromagnetic contaminants in water. Building on a previously validated device integrating a tunneling magnetoresistance (TMR) sensor and a permanent magnet, this study focuses on optimizing the energy efficiency of the sensing node to enable long-term, battery-powered field deployments. The complete sensing system, including the analog front-end, an ATtiny84 microcontroller, and an RFM95 LoRa radio module, is experimentally characterized to identify the dominant contributors to energy consumption. Results show that, while the sensing front-end exhibits a moderate current draw, the LoRa transmission phase and the MCU active time represent the main energy bottlenecks. Based on these findings, a power-saving strategy combining sensing-chain duty cycling, MCU low-power sleep modes, and a realistic transmission interval is implemented. Experimental measurements demonstrate a reduction of the average system current to approximately 0.48 mA, enabling an estimated battery lifetime of more than six months using a standard 2200 mAh Li-ion cell, without degrading measurement fidelity. These results validate the feasibility of an energy-aware design for autonomous magnetic probes and support their applicability for long-term monitoring of ferromagnetic pollutants in natural water environments.

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.

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.

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.

The present study proposes the implementation of an air quality measurement tool through the use of wearable devices, named WeAIR, consisting of wearable sensors for measuring NOx, CO2, CO, temperature, humidity, barometric pressure and PM10. In particular through the use of our novel sensor prototype, we performed a measurement collection campaign, acquiring an extensive set of geo-localized air quality data in the city of Siena (Italy). We further implemented and applied an AI neural network based model, capable of predicting the localization of an observation, having as input the air monitoring parameters and using the new spatiotemporal collected datasets. The promising performances obtained with the AI prediction approach enhanced the importance and possibilities of using such spatio-temporal air quality monitoring datasets, suggesting their crucial role both for raising citizen awareness on climate change and supporting policymakers' decisions, as for instance the ones related to the positioning of new fixed monitoring stations.

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.

Quartz crystal microbalance with dissipation (QCM-D) monitoring is a sensing technique that finds diverse applications in chemical sensing, biosensing, and industrial fields. By monitoring the vibrational characteristics of a thin piezoelectric SiO2 resonator, this technique provides valuable information about the viscoelastic behavior of media in contact with its surface. This article evaluates the performance of algorithms applied to 10 MHz AT-cut QCM-D signals for estimating the frequency and time constant related to the fundamental resonant mode, extending the study to assess the effectiveness of disturbance rejection, particularly against quartz spurious oscillating modes. To this end, both damped single-tone and three-tone signals are analyzed to cover all potential outputs of a QCM-D in its fundamental bulk shear mode. Various estimation algorithms are considered: one operating in the frequency domain and others in the time domain, based on model fitting techniques with novel preprocessing steps for optimized initialization. The analysis utilizes signals from a QCM-D measurement set-up and signals simulated according to the characteristics of the AT-CUT crystals used. Among model fitting-based estimators, two techniques were developed for single-tone and three-tone signals, splitting the model-fitting problem into nonlinear and linear subproblems, demonstrating the best accuracy and processing time. The developed techniques meet strict measurement objectives, achieving frequency measurement resolution around 1 ppm (10 Hz) and decay time accuracy around 1 mu s, even with additive white Gaussian noise (AWGN) standard deviations of about 1% of the fundamental tone amplitude in water. These techniques are applicable in various contexts where recovering parameters from damped sine waves in the presence of white noise is required.

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 work presents a nanostructured $\text{WO}_{3}$-based chemoresistive sensor as a low-power alternative to NDIR systems for $\text{CO}_{2}$ monitoring in IoT applications. Thanks to its nanograined morphology, the sensor achieves measurable response below 220° C, with enhanced sensitivity in nitrogen due to reduced oxygen interference. Pulsed-temperature operation further improves performance while minimizing energy consumption, enabling integration into compact, batterypowered platforms for distributed air quality sensing.

Quartz Crystal Microbalance (QCM) is a pivotal technique for analyzing tribological interactions and lubricant performance at both macroscopic and nanoscopic scales. This study explores the limitations of traditional QCM methods when applied to highly viscous lubricants and proposes an innovative measurement method utilizing chirped excitation signals to enhance sensitivity when analyzing highly damped QCMs. The proposed method involves exciting the quartz sensor with a sinusoidal linear frequency sweep and measuring current through and voltage across the quartz with a tailored front-end electronics. This approach allows allowing for more accurate characterization of lubricants with high viscosity coefficients. Experimental validation was conducted using a laboratory testbench with AT-cut 10 MHz quartzes, demonstrating the effectiveness of the new approach in measuring viscoelastic properties exploiting engine oils with different degrees of wear. The results indicate that the chirped excitation method provides a robust and scalable solution for lubricant characterization, overcoming the challenges faced by conventional QCM-D techniques. This approach offers significant improvements in sensitivity and accuracy, particularly in highly viscous regimes, making it a valuable tool for both academic research and industrial applications. The study highlights the potential of modified QCM hardware to expand the range of lubricant characterization, providing deeper insights into the behavior of base oils and additive-enhanced lubricants under various operating conditions.

Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) is a widely used technique for studying interfacial phenomena, particularly in tribology, thin-film characterization, and biosensing. While conventional QCM-D measurements focus on the fundamental thickness-shear mode, quartz crystals also exhibit spurious resonances that respond differently to external loads. In this work, we propose a novel approach to excite and analyze the first spurious mode of a QCM sensor to assess its sensitivity to mechanical loading. Our results show that, unlike the fundamental mode, the spurious resonance exhibits a non-linear relationship between equivalent inductance and resistance, with its frequency shift increasing significantly under higher loads. This suggests a stronger sensitivity to dissipation and viscoelastic effects at the solidliquid interface. Furthermore, we demonstrate that the proposed method achieves sufficient accuracy (tone estimation standard deviation lower than 1 ppm and resistance estimation standard deviation lower than 1%) using simple signal processing techniques, providing a practical alternative to impedance spectroscopy. These findings highlight the potential of spurious resonances to complement standard QCM-D measurements, offering enhanced capabilities for characterizing thin films, lubricants, and viscoelastic materials.

The estimation of the CO2 concentration variations due to plants photosynthesis has become a topic of outstanding interest in view of evaluating the carbon footprint of greenhouses and farms. In this framework, distributed sensing solutions can be an alternative to expensive and power-consuming gas analyzers. In this article, a low-cost sensor node thought for the accurate monitoring of photosynthesis-related quantities (i.e., temperature, pressure, water vapor concentration, and CO2 concentration) is presented. The system is validated through measurement campaigns involving plants enclosed in an accumulation chamber in the presence of artificial light with controlled intensity, photoperiod, and spectral composition. A simplified preliminary model is also proposed to interpret the obtained results from the point of view of the kinetics of the gas exchanges, achieving the estimation of the CO2 uptake and release rates ascribable to the plants activity.

In this work we propose use of three advanced digital fitting algorithms for the extraction of frequency and time constant of QCM-D response signals. The proposed algorithms provide very low estimation errors so to reach the challenging resolution and accuracy required in QCM applications. The algorithms' performance was characterized using simulated, emulated, and experimental datasets. The paper analyzes the dependence of the estimation accuracy both on the noise level and on the measurement conditions in terms of mechanical load. It is shown that with a smart selection of the initial conditions for the fitting algorithms, an estimation error lower than 5 Hz is reached for the series frequency in the presence of typical noise levels, even in the presence of large mechanical loads.