# Fingerprint-Based Localization Approach for WSN Using Machine Learning Models
## 5 Experimental Results
However, according to the obtained localization accuracy, the large RSS dataset offers the best average localization accuracy compared to the small and medium RSS datasets, and this refers to the fact that the number of reference points is greater in the large RSS dataset, which assists the ML model to train on sufficient cases. Table 11 presents the detailed average localization error in meters when employing the four ML models with the three different datasets. For more evaluation analysis, the trained KNN model was tested in another indoor testbed that is presented in Figure 14, which is a study room located in the IIRC lab with a dimension of \(14.1\times 3.92\) m\({}^{2}\) and consists of a number of desks and chairs. A total number of four reference nodes were distributed in the corners of the study room, and a single mobile node (target node) was employed for the validation process. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Small Dataset** & **Medium Dataset** & **Large Dataset** \\ \hline LR & 2.10 & 3.65 & 2.75 \\ RF & 2.00 & 2.80 & 4.60 \\ KNN & 2.50 & 2.21 & 1.40 \\ DT & 3.06 & 3.30 & 3.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Average localization error (in meters) for the 4 tailored ML models using 3 different datasets. Figure 13: Average localization error for 4 localization methods using the 3 RSS datasets. For evaluation purposes, 20 different testing-points were positioned. Then, the RSS values were collected from each test point and fed into the tailored KNN model in order to perform localization estimation. The localization error was estimated through measuring the difference between the estimated and actual coordinates. As presented in Figure 15, the localization error was estimated for 20 test points, with an average localization error of 1.5 m.  
On the other hand, the localization accuracy was analyzed for the developed system in this paper and the recent AI-based fingerprinting approaches. Table 12 presents a comparison between the developed fingerprinting localization approach that is based on the tailored KNN ML model and the recently developed ML-based fingerprinting localization approaches. As discussed above, most of the recently developed systems were validated through simulation experiments. In addition, the average localization error was 0.3-6 m. However, the developed system in this paper was practically validated through real experiments conducted in two different indoor environments, where the average localization error was around 1.4 m. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Research Work** & **Experiment Testbed** & **Localization Error (Meter)** \\ \hline
[25] & Simulation experiments & 2 m \\  & Real experiments using the CC2530 ZigBee & 1.7 m \\  & nodes & \\
[27] & Simulation experiments—MATLAB & 0.3 m \\
[28] & Simulation & 2.5 m \\
[31] & Simulation through real datasets & 4 m \\
[32] & Simulation experiments & 6 m \\
[33] & Simulation experiments & 5 m \\
[34] & Simulation experiments & 4.5 m \\ This work & Real experiments—ZigBee Series 2 nodes & 1.4 m \\ \hline \hline \end{tabular}
\end{table} 
Table 12: A comparison between the developed fingerprinting localization approach and recent AI-based localization systems. Figure 14: The actual structure of the study room. Figure 15: The localization error for 20 test points in the study room testbed.