This paper shows the feasibility of a High Frequency RFID passive sensor tag equipped with sensors based on a thin film circuit on a Si substrate for the measurement of the soil moisture. Three tags embedding different sensing systems compliant with the energy harvesting requirements were developed. The used sensors were previously characterized with the aim of tailoring an adequate electronic front-end. All the typical solutions for soil moisture detection were implemented: a fringe capacitance sensor, a soil impedance sensor and finally a humidity sensor based on functionalization with TiO2 nano particles. The obtained results prove that the conceived solution is satisfactory and that the sensors have good sensing properties. The proposed system can be applied within the IoT framework in manifold monitoring scenarios of soil moisture sensing.

In this paper, a novel measurement system based on Quartz Crystal Microbalances is presented. The proposed solution was conceived specifically to overcome the measurement problems related to Quartz Crystal Microbalance (QCM) applications in dielectric liquids where the Q-factor of the resonant system is severely reduced with respect to in-gas applications. The QCM is placed in a Meacham oscillator embedding an amplifier with adjustable gain, an automatic strategy for gain tuning allows for maintaining the oscillator frequency close to the series resonance frequency of the quartz, which is related in a simple way with the physical parameters of interest. The proposed system can be used to monitor simultaneously both the series resonant frequency and the equivalent electromechanical resistance of the quartz. The feasibility and the performance of the proposed method are proven by means of measurements obtained with a prototype based on a 10-MHz AT-cut quartz.

This paper aims at demonstrating the energy self-sufficiency of a LoRaWAN-based sensor node for monitoring environmental parameters exploiting energy harvesting directly coming from the artificial light used in indoor horticulture. A portable polycrystalline silicon module is used to charge a Li-Po battery, employed as the power reserve of a wireless sensor node able to accurately monitor, with a 1-h period, both the physical quantities most relevant for the application, i.e., humidity, temperature and pressure, and the chemical quantities, i.e., O₂ and CO₂ concentrations. To this aim, the node also hosts a power-hungry NDIR sensor. Two programmable light sources were used to emulate the actual lighting conditions of greenhouses, and to prove the effectiveness of the designed autonomous system: a LED-based custom designed solar simulator and a commercial LED light especially thought for plant cultivation purposes in greenhouses. Different lighting conditions used in indoor horticulture to enhance different plant growth phases, obtained as combinations of blue, red, far-red and white spectra, were tested by field tests of the sensor node. The energy self-sufficiency of the system was demonstrated by monitoring the charging/discharging trend of the Li-Po battery. Best results are obtained when white artificial light is mixed with the far-red component, closest to the polycrystalline silicon spectral response peak.

In this paper, we present a smart gravimetric system for the automatic security monitoring of the accesses to public places with some entrance ticket or pass required (e.g. railway stations, subway stations, museums, exhibitions). The main objective is to spot illegal behaviors, such as two people trying to enter using only one ticket; simultaneously, other events of interest can be detected, such as the passage of a person with disabilities or a person with a stroller. These pieces of information can be used to arrange the appropriate assistance or to conduct statistical analyses on the monitored place frequentation. The proposed system is based on a robust gravimetric footboard, composed of steel plates laid on load cells. The data processing, classification included, is totally executed on embedded STM32 microcontrollers, following the edge computing paradigm. The classification exploits a MobileNetV2, a class of neural networks for Computer Vision, specially created for embedded and mobile applications. We realized a prototype of the gravimetric system for laboratory testing and we acquired a set of samples for the considered event classes to assess the MobileNet network capabilities. We compared the performance of this implementation with a more classic VGG16 convolutional neural network running on a PC machine. We show that, in the face of akin classification results and similar execution time between the two, the embedded MobileNet version brings many advantages, namely portability, cost, size and energy consumption reduction and finally simplification in the installation procedure.

This study presents a flexible 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 panel and on CdS light dependent resistors. 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.

Here, we propose a novel application of a low-cost robust gravimetric system for public place access monitoring purposes. The proposed solution is intended to be exploited in a multi-sensor scenario, where heterogeneous information, coming from different sources (e.g., metal detectors and surveillance cameras), are collected in a central data fusion unit to obtain a more detailed and accurate evaluation of notable events. Specifically, the word “notable” refers essentially to two event categories: the first category is represented by irregular events, corresponding typically to multiple people passing together through a security gate; the second category includes some event subsets, whose notification can be interesting for assistance provision (in the case of people with disabilities), or for statistical analysis. The employed gravimetric sensor, compared to other devices existing in the literature, exhibits a simple scalable robust structure, made up of an array of rigid steel plates, each laid on four load cells. We developed a tailored hardware and software to individually acquire the load cell signals, and to post-process the data to formulate a classification of the notable events. The results are encouraging, showing a remarkable detectability of irregularities (95.3% of all the test cases) and a satisfactory identification of the other event types.