# An IoT Reader for Wireless Passive Electromagnetic Sensors
## 3 Results  
After testing the different reader layers separately, the whole IoT-R is assembled. The experimental setup of the IoT-R system is shown in Figure 21 including the radiating layer, the RF interface, the mini-computer, the _CLIENT_ computer and the wireless sensor. The different active layers are biased by the power unit. The used power unit is based on LM350 AT regulators [45]. Finally, the IoT-R is connected to the Internet through the WiFi interface, as explained in Section 2.4. In order to test the validity of the whole system for the proposed liquid sensing application, the submersible sensor is immersed in different LUTs. Table 2 shows the measured samples as well as their relative permittivities. It can be noticed that the relative permittivity of the selected samples is between 1 and 6. The measured results are remotely retrieved from the Internet by running a _CLIENT_ in a laptop. This _CLIENT_ is programmed in Matlab. A different execution of the _CLIENT_ program is run for each sensor immersion.  
Figure 22 shows the measurements of the whole sensing system for the different samples. A single period of the data received by the _CLIENT_ for each sample is represented. The triangular signal (_sync_) is similar for all the samples and is proportional to the signal which controls the frequency sweep (\(V_{\textit{TUNE}}\)). In particular, two frequency sweeps are performed per period: the first one from 1.6 to 1.9 GHz (increasing sweep) and another one from 1.9 to 1.6 GHz (decreasing sweep). In each sweep, a minimum voltage occurs at a different time for each measurement: \(t_{1}\) in the increasing sweep, and \(t_{2}\) in the decreasing sweep. These minima are related to the resonant frequency of the sensor for different LUTs, as explained in Section 2.3. Thus, the detection of the whole sensing system relies in the time difference between \(t_{2}\) and \(t_{1}\) (\(\Delta t=t_{2}-t_{1}\)). In Table 2, the time difference, \(\Delta t\), related to each sample is included. It can be seen a time difference between 3.59 ms and 8.04 ms for values of relative permittivity between 1 and 5.77, corresponding to free space and \(\text{CHCl}_{3}\) respectively. Each measured sample presents a different \(\Delta t\). Differences are detected even in the case of almond and lemon oils, whose relative permittivities are close.  
Figure 21: Demonstrator containing all the system elements.  
Figure 23 shows the dependency of the relative permittivity with the time difference (\(\Delta t\)) for each sample. The measured results show a linear relation between the time difference and the relative permittivity of each sample. For this reason, the linear approximation of Equation (5) is also included. This approximation can be used to easily estimate the relative permittivity of the sample, \(\varepsilon_{r}\), after the measurement of the time difference between the two minima detected in the periodic response signal. The estimation of \(\varepsilon_{r}\) can be done by using the Equation (6). The resulting mean error between the measurements and the fitted curve is 1.71%.  
\[\Delta t[\mathrm{ms}]=0.93\cdot\varepsilon_{r}+2.66 \tag{5}\]  
\[\varepsilon_{r}=1.07\cdot\Delta t[\mathrm{ms}]-2.86 \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Material** & **Relative Permititivity (\(\varepsilon_{r}\))** & \(t_{1}\) **[ms]** & \(t_{2}\) **[ms]** & \(\Delta t\) **(\(t_{1}-t_{2}\)) [ms]** \\ \hline Free Space & 1 & 3.48 & 7.07 & 3.59 \\ Paraffin Oil & 2.45 & 2.76 & 7.91 & 5.16 \\ Almond Oil & 2.96 & 2.52 & 8.03 & 5.51 \\ Lemon Oil & 3.03 & 2.40 & 8.03 & 5.63 \\ CHCl\({}_{3}\) & 5.77 & 1.32 & 9.36 & 8.04 \\ \hline \end{tabular}
\end{table}
Table 2: Results obtained with the whole sensing system.  
Figure 22: Measurements of the whole system (signals received by the _CLIENT_).