## 4 Testing Scenario  
In order to evaluate the accuracy of the NLP, a set of experiments were devised to test the NLP engine under a plurality of scenarios. As the network location provider provides not only latitude and longitude, but also altitude, an assessment was carried out for both horizontal and vertical accuracies. For the horizontal accuracy, a drive test was carried out in rural, suburban, and urban environments with varying amounts of Wi-Fi and cell coverage. For the rural scenario, a route around Erie, Colorado, was selected. This was because most of the route was through farmlands, which meant that the number of visible Wi-Fi access points is limited. For the suburban scenario, a drive through Boulder, Colorado, was chosen. This is due to the presence of many small businesses and residential houses along the route. For the urban scenario, Denver, Colorado, was chosen due to the large number of high-rise buildings and numerous corporate networks located in the city center. For the vertical accuracy, a drive test was carried out near Boulder, Colorado, where there are altitude variations between 1620 m and 1720 m in a less than 10-min time period. A summary of the scenarios is provided in Table 1 and the route visualizations are provided in Figure 3. Each test was approximately one hour long and was carried out on 11 December 2021.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Details** & **Network Availability** \\ \hline Rural & Rural area near Erie, Colorado & Sparse Wi-Fi, Cell \\ \hline Suburban & Downtown Boulder, Colorado & Moderate Wi-Fi, Cell \\ \hline Urban & Downtown Denver, Colorado & Dense Wi-Fi, Cell \\ \hline Altitude Variation & Highway around Boulder, Colorado & Moderate Wi-Fi, Cell \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of all of the scenarios investigated in this study.  
The device used to log the data was a pair of Samsung S21 Ultra 5G. The phone had Wi-Fi and cell turned on, and the \"Wi-Fi scanning\" option which \"lets apps use Wi-Fi for more accurate location detection, even when Wi-Fi is off\" was enabled. The data was logged using a custom logger that logs information provided by the Android Network Location Provider and all visible Wi-Fi and cell access points.  
For truth comparison, a u-blox C099-P9P receiver with a ZED-F9P GNSS module and a L1/L2 multi-band, high precision GNSS antenna was used for data collection. The data collected was processed using RTKLib PPP-RTK dynamic mode with NIST, Colorado, as a reference DGNSS station. As the expected accuracy of the u-blox is within 0.01 m with RTK and has a navigation update rate of 20 Hz, it was deemed sufficient to serve as a truth reference for this study [34]. Furthermore, although the performance of GNSS receivers is limited in signal-attenuated environments where no GNSS signals are available, or in the presence of spoofing, the authors verified that the dataset collected had 100% GNSS availability, and no form of GNSS spoofing.  
Figure 3: The routes used for each of the test scenarios. (**A**) Erie, Colorado, with limited Wi-Fi and Cell coverage; (**B**) downtown Boulder, Colorado, with many residential network coverage; (**C**) downtown Denver, Colorado, with many corporate and business network coverage; (**D**) a loop with a lot of elevation changes;.