## 5 Results
### Accuracy Results  
By utilizing the obtained radio maps of the experimental area and the various routes, the coordinates of the robot will be predicted using the ML methods outlined in Section 3.4. The prediction results of the models are evaluated using the coefficient of determination R\({}^{2}\) for each method outlined in Section 3.2. Table 2 presents the ML results as R\({}^{2}\) scores for three scenarios, using different pre-processing methods and configurations. The GRU method combined with the Sector_256 configuration achieves the highest accuracy across all models and scenarios. This indicates that the GRU's recurrent architecture is particularly effective for mobile node localization in wireless sensor networks, likely due to its ability to retain long-term dependencies, such as past RSSI measurements. Additionally, it can be concluded that the proposed sectorization method enhances prediction accuracy. For example, the GRU model achieves the highest R\({}^{2}\) scores of 0.9454 in the first scenario, 0.9102 in the second, and 0.8512 in the third when using the Sector_256 configuration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ML** & **Scenario** & **EKF** & **No Filter** & **Sector\_256** & **Sector\_1024** \\ \hline \multirow{4}{*}{GRU} & First & 0.8598 & 0.9298 & 0.9454 & 0.9354 \\ \cline{2-6}  & Second & 0.7324 & 0.8595 & 0.9102 & 0.8902 \\ \cline{2-6}  & Third &  
The 1024-sector division method shows solid results: 0.9354 in the first scenario, 0.8902 in the second, and 0.8412 in the third. Although Sector_256 outperformed Sector_1024, it can be anticipated that the localization accuracy will improve with Sector_1024, as a greater number of sectors enables more precise positioning of the nodes. The ML results further suggest that the application of the EKF for preprocessing does not yield significant improvements compared to models trained without the filter. This is likely to be due to the dynamic fluctuations in RSSI values caused by the movement of the mobile node. Figure 14 visualizes the performance of the ML models using bar charts, providing a clearer comparison of their accuracy across the scenarios. \begin{table}
\begin{tabular}{c c c c c c} \hline
**ML** & **Scenario** & **EKF** & **No Filter** & **Sector\_256** & **Sector\_1024** \\ \hline \multirow{3}{*}{SVR} & First & 0.8529 & 0.9218 & 0.9032 & 0.8976 \\ \cline{2-6}  & Second & 0.6484 & 0.8382 & 0.8566 & 0.8487 \\ \cline{2-6}  & Third & 0.5247 & 0.7167 & 0.7712 & 0.7885 \\ \hline \multirow{3}{*}{BPNN} & First & 0.8068 & 0.8330 & 0.8983 & 0.8912 \\ \cline{2-6}  & Second & 0.6940 & 0.8195 & 0.8117 & 0.7862 \\ \cline{2-6}  & Third & 0.5462 & 0.5318 & 0.7641 & 0.7510 \\ \hline \multirow{3}{*}{RNN} & First & 0.8310 & 0.8957 & 0.9200 & 0.9230 \\ \cline{2-6}  & Second & 0.7620 & 0.8497 & 0.8579 & 0.8283 \\ \cline{2-6}  & Third & 0.4946 & 0.7212 & 0.7639 & 0.8165 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 14: ML results (R\({}^{2}\) score) for various types of routes: (**a**) first (**b**) second (**c**) third. Figures 15-17 present the machine learning results using MAE and RMSE metrics for the first, second, and third scenarios, respectively. The X-axis represents the sequential position numbers traversed by the mobile robot. The first scenario includes 104 points, the second 157 points, and the third 736 points. From the analysis of the graphs, it can be concluded that the GRU and MLP models deliver the best performance compared to other ML models. For the first scenario, the best performing models are GRU, achieving an average mean absolute error of 0.246 m and a root mean square error of 0.300 m, and SVR, with an average mean absolute error of 0.253 m and a root mean square error of 0.315 m. The worst model is BPNN, which has an average mean absolute error of 0.316 m and a root mean square error of 0.378 m.  
For the second scenario, GRU and MLP deliver the best results.