## 3 Performance Analysis
### Empirical Radio Frequency Data Collection in the Park of the Checa Parish  
Checa Park is distinguished by its flat topography, sparse vegetation, and variable climate that ranges from cold to subtropical. The area has low vehicular and pedestrian traffic, which categorizes it as a rural region. The equipment used for data collection was built using a Raspberry Pi 3, a GPS module, and a Python script. This setup allows for the acquisition of latitude, longitude, and the power level emitted by a hotspot as measured by the Raspberry Pi 3 wireless card from a nearby location to the transmitter. The data, which include power levels and their respective geolocations, are stored and subsequently exported to a text file. Finally, this information is processed using a Matlab script to perform calculations and generate heatmaps. The following procedure was conducted for the data collection:  
* Examination of the card and the power level detection software. Connection of the power supply to the vehicle to facilitate drive assessment. * Execution of the drive test in regions proximal to the hotspot to obtain data regarding power levels. * Georeferencing of specific points within the area to produce a topographic map of the region that encompasses the hotspot in the Checa parish. * Subsequent data analysis. Figure 4: Received power vs distance according to the SUI mode. Parameters: receiver height \(h_{\text{m}}\) = 1.7 m, frequency \(f\) = 2.412 GHz, and Category C suburban areas [Equation (6)]. Using the Raspberry Pi module and GPS, a drive test was performed, which yielded 560 samples around the hotspot at an average speed of 3 km/h. Table 4 illustrates the structure of the data obtained from the measurement of power levels using the Raspberry Pi 3. After data post-processing, Figure 5 presents a graph depicting the empirically measured power levels as a function of distance from the receiver. Based on the empirical data collected using Raspberry Pi 3, Figure 5 shows that signal levels vary between \(-40\) dBm and \(-100\) dBm, which, when compared to the SUI model, do not align closely with the model's expectations. Propagation Model Using Data from a Wi-Fi Hotspot in Checa Park and Curve Fitting Techniques in Matlab  
The development of this propagation model was informed by empirical data collection at the Checa Park hotspot. This enabled a refinement of the SUI model parameters, thus improving precision in the computation of Wi-Fi signal power losses. Utilizing the collected data, a MATLAB function was employed to amalgamate both deterministic and empirical data to formulate a propagation model. This model elucidates the probabilistic pattern of spectrum occupancy, accommodating variations in terrain and equipment utilized within the study. In this context, the Curve Fitting Toolbox was utilized, encompassing an application and a variety of functions designed to fit curves and surfaces to the data. This toolbox facilitates exploratory data analysis, pre-processing and post-processing of data, model comparison, and the elimination of outliers. The fit-type function was specifically used to fit curves and surfaces, thereby generating a probabilistic logarithmic function. Consequently, based on the applied code and functions, a novel equation was derived, culminating in the adjusted model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Column A** & **Column B** & **Column C** \\ \hline
0, “UdaCheca” & QUALITY: 43/79 & Signal: \(-67\) \\ Latitude = \(-0.127112\) and Longitude = \(-78.312685333\) & & \\
0, “UdaCheca” & QUALITY: 51/70 & Signal: \(-59\) \\ Latitude = \(-0.12711111\) and Longitude = \(-78.312647666\) & & \\
0, “UdaCheca” & QUALITY: 43/70 & Signal: \(-67\) \\ Latitude = \(-0.1271234\) and Longitude = \(-78.31264652\) & & \\
0, “UdaCheca” & QUALITY: 53/70 & Signal: \(-57\) \\ Latitude = \(-0.127195\) and Longitude = \(-78.31264705\) & & \\ \hline \hline \end{tabular} Raw Data from the text file of the Raspberry Pi 3.  
\end{table}