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.

This paper presents a robust Visible Light Localization (VLL) technique for wireless sensor networks, with 2-D Indoor Positioning (IP) capabilities, based on embedded Machine Learning (ML) running on low-cost low-power microcontrollers. The implemented VLL technique uses four optical sources (i.e., LEDs), modulated at different frequencies. In particular, the received signal strengths (RSSs) of optical signals are evaluated by a microcontroller on board the sensor nodes via fast Fourier transform (FFT). RSSs are fed to four embedded ML regressors, aiming at estimating the target position within the workspace. The four Neural Networks (NNs), one per each possible triplet of LEDs, are trained by exploiting a novel technique to generate the training datasets. This method, called Optimized Fingerprinting (OF), allows for creating arbitrarily ample datasets by performing only few measurements in the field, avoiding time-consuming steps for collecting experimental data. The NNs are devised to be accurate yet lightweight facilitating their implementation and execution by the microcontroller. Furthermore, thanks to the presence of four NNs, four position estimates are obtained. This redundancy is exploited to detect and effectively manage situations of total or partial shading of one light source and to enhance the positioning accuracy under normal operating conditions (i.e., no obstacles), by averaging the four positions. Test results performed in a 1 m x 1 m workspace show an overall mean accuracy of about 1 cm with standard deviation below the centimeter and maximum error around 3 cm.

TiO₂ nanoparticles coating has been proven to be an extremely performing sensing material for relative humidity (RH) measurements. The chemical activity of TiO₂ toward water vapor adsorption and the very large surface to volume ratio typical of nanostructures are ideal characteristics for the development of RH fast and sensitive sensors. Different sensor technologies can be used in conjunction with this material to realize devices with satisfactory performance. In this paper, the authors aim to describe and discuss the main different possible choices and highlight the advantages and disadvantages, and linking them both to the underlying mechanism of water adsorption on the TiO₂ sensing layer and to the modification of the electrical behavior due to the water adsorption. In particular, the authors start from results obtained by depositing TiO₂ nanoparticles on a novel MEMS microbalance operating at low frequency, which allows to sense only the adsorbed water mass, and they exploit the sensor output to obtain a dynamic model of the water adsorption. They also link these results to those obtained with a Quartz Crystal Microbalance (QCM) functionalized with the same material operating at 10 MHz as a part of an oscillator. Finally, they establish a link with the results obtained by an RH impedance sensor, which exploits the same active material and the same deposition technique. With this sensor technology, the conductive and electrical behavior of the sensing and adsorbed films play a role. The whole work tries to unravel the different phenomena that contribute to the response of RH sensors not only based on TiO₂ nanoparticles but also, more generally, based on nanostructured metal oxide materials.

Direct measurements of aeolian sand transport on coastal dunes and beaches is of paramount importance to make correct decisions about coast management. As most of the existing studies are mainly based on a statistical approach, the solution presented in this paper proposes a sensing structure able to orient itself according to wind direction and directly calculate the amount of wind-transported sand by collecting it and by measuring its weight. Measurements are performed remotely without requiring human action because the structure is equipped with a ZigBee radio module, which periodically sends readings to a local gateway. Here data are processed by a microcontroller and then transferred to a remote data collection centre, through GSM technology. The ease of installation, the reduced power consumption and the low maintenance required, make the proposed solution able to work independently, limiting human intervention, for all the duration of the expected experimental campaign. In order to analyze the cause-effect relationship between the transported sand and the wind, the sensing structure is integrated with a multi-layer anemoscope-anemometer structure. The overall sensor network has been developed and tested in the laboratory, and its operation has been validated in field through a 48 h measurement campaign.

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.