This paper provides for the first time a very extensive experimental characterization coupled with a fully analytical modeling in order to investigate, in a systematic and comprehensive way, the sensing performance of a two-port surface acoustic wave (SAW) resonator from room down to cryogenic temperatures. The motivation behind this work is twofold: to quantitatively assess the temperature sensitivity of the SAW technology for cryogenic applications and to gain a better understanding of the underlying physics in terms of the equivalent-circuit elements. Although the measurement-based analysis is developed by focusing on a SAW from Murata as a case study, the developed investigation methodology is independent of the considered technology and extensible to other SAW types. A cryogenic system based on a closed-loop helium refrigerator is used to cool the tested SAW from 300 K down to 20 K with a step of 10 K. At each studied temperature, the scattering parameters are measured using a vector network analyzer over a narrow frequency band around the nominal resonant frequency of 423.2 MHz, spanning from 420 MHz to 425 MHz with a small step of 3.125 kHz. The measured scattering parameters are then transformed into the admittance ones, as they are more useful for sensing performance assessment and for equivalent-circuit model extraction. The extracted model is successfully validated through the achieved good agreement between measurements and simulations of the temperature- and frequency-dependent behavior of the studied resonator.

The studied sensor consists of a microstrip interdigital capacitor covered by a gas sensing layer made of titanium dioxide (TiO₂). To explore the gas sensing properties of the developed sensor, oxygen detection is considered as a case study. The sensor is electrically characterized using the complex scattering parameters measured with a vector network analyzer (VNA). The experimental investigation is performed over a frequency range of 1.5 GHz to 2.9 GHz by placing the sensor inside a polytetrafluoroethylene (PTFE) test chamber with a binary gas mixture composed of oxygen and nitrogen. The frequency-dependent response of the sensor is investigated in detail and further modelled using an artificial neural network (ANN) approach. The proposed modelling procedure allows mimicking the measured sensor performance over the whole range of oxygen concentration, going from 0% to 100%, and predicting the behavior of the resonant frequencies that can be used as sensing parameters.

In the present study, the inkjet printing (IJP) technique is used for the development of a planar microwave sensor aimed at the dielectric characterization of biological samples. The proposed sensor consists of two capacitively coupled split-ring resonators fabricated using the microstrip technology. A silver-based conductive ink is deposited by inkjet printing on a conventional 1.6-mm thick FR4 substrate, thus creating the resonant structure. The experimental analysis is carried out by considering a water-ethanol mixture as a test solution in which the ethanol volume fraction is varied, thereby changing the effective permittivity of the mixture. In this contribution, the authors focused their analysis on the dielectric constant of the solution under test (i.e., the real part of the complex relative permittivity). The water-ethanol mixture is placed into low-density polyethylene (LDPE) sample vial which is arranged on the surface of the planar sensor. No direct contact is needed between the microwave device and the solution under test. The effect of the ethanol volume fraction change is related to the behavior of the two resonances occurring in the forward transmission coefficient ( S₂₁ ) of the sensor in the frequency range from 2 GHz to 3 GHz. The permittivity change of the sample under test affects the capacitive coupling between the two split-ring resonators altering the separation between two resonant frequencies. This enables the sensor to differential measurements, thereby improving its robustness. The frequency separation is used to track variations in the ethanol concentration and, thus, the dielectric constant of the solution under test. Additionally, a lumped-element equivalent-circuit model is presented and the changes in the values of the lumped elements with ethanol concentration are estimated and discussed.

In industrial wireless sensors networks (IWSNs) deployments, data management and processing tasks are often delegated to centralized computing facilities. Albeit providing an abundance of resources, these solutions impose several drawbacks, stemming from the higher latency they may introduce, as well as from security/privacy issues. To deal with these shortcomings, in this article, we propose an architecture designed for industrial monitoring duties, based on the Fog computing paradigm. In particular, we devised a Fog layer to host a data aggregation algorithm based on singular value decomposition (SVD). Considering a real-world case study about a distributed monitoring system for industrial motors, the benefits of the proposed solution in terms of network performances, i.e., latency and packet delivery ratio, are analyzed using the OMNET++ simulator. Besides, the system implements a set of security mechanisms at different levels (i.e., IWSN, Fog, and Cloud). In particular, we propose a new lightweight and energy-efficient ciphering algorithm for resource-constrained industrial motes. According to our experiments, the new algorithm reduces energy consumption by a factor of three compared to Advanced Encryption Standard-128.

In this paper we present a novel autocorrelation-based frequency estimation algorithm for single-tone sinusoidal signals. In comparison to other state-of-the-art frequency estimation methods, the proposed one provides a better trade-off between accuracy, complexity and estimation range. In particular, the algorithm is able to achieve the Cramer-Rao lower bound for moderate and high signal to noise ratio and its implementation is feasible even in resource-constrained microcontrollers, as those commonly used in Industrial Internet of Things (IIoT) applications and low-cost instrumentation. Finally, we investigate performance of the algorithm in the case of a practical IIoT application, i.e. frequency estimation of unbalanced three-phase power systems, showing that it outperforms several other autocorrelation-based estimators.