# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 4 Experimental Results and Discussions
When the accuracy rates obtained were analyzed, it was observed that in Dataset 1 \begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Wi-Fi Dataset** & **Wi-Fi + Room-to-Room** \\ \hline Fine Tree & 81.9 & 92.9 \\ \hline Efficient Logistic Regression & 82.1 & 83.4 \\ \hline Weighted k-NN & 81.9 & 93.6 \\ \hline Linear SVM & 81.9 & 89.6 \\ \hline Wide Neural Network & 82.3 & 94.7 \\ \hline Ensemble Bagged Trees & 82 & 93.1 \\ \hline \end{tabular}
\end{table} 
Table 6: Accuracy scores of Wi-Fi dataset and Wi-Fi + room-to-room time for Dataset 2, using 10-fold cross-validation. \begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Wi-Fi Dataset** & **Wi-Fi + Room-to-Room** \\ \hline Fine Tree & 81 & 83.9 \\ \hline Linear Discriminant & 88.8 & 89.4 \\ \hline Cosine k-NN & 88.7 & 89.6 \\ \hline Medium Gaussian SVM & 89.9 & 90.5 \\ \hline Wide Neural Network & 78.2 & 85.6 \\ \hline Ensemble Bagged Trees & 85.8 & 88.5 \\ \hline \end{tabular}
\end{table} 
Table 7: Accuracy scores of Wi-Fi dataset and Wi-Fi + room-to-room time for Dataset 3, using 10-fold cross-validation. \begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Wi-Fi Dataset** & **Wi-Fi + Room-to-Room** \\ \hline Fine Tree & 82.9 & 86.7 \\ \hline Linear Discriminant & 87.5 & 88.7 \\ \hline Coarse k-NN & 83.6 & 84.7 \\ \hline Medium Gaussian SVM & 88.5 & 89. \\ \hline Wide Neural Network & 79.8 & 83.2 \\ \hline Ensemble Bagged Trees & 87.2 & 89.9 \\ \hline \end{tabular}
\end{table} 
Table 5: Accuracy scores of Wi-Fi dataset and Wi-Fi + room-to-room time for Dataset 1, using 10-fold cross-validation. incorporating room-to-room transition time resulted in moderate accuracy improvements across all the tested models. The Ensemble Bagged Trees and Medium Gaussian SVM models demonstrated the highest improvements, with accuracy increasing from 87.2% to 89.9% and from 88.5% to 89.5%, respectively. This suggests that Ensemble-based and kernel-based approaches effectively leverage the additional temporal feature. The Fine Tree model also showed a meaningful gain, improving from 82.9% to 86.7%, indicating that Decision Tree-based models benefit from the added contextual information of transition time. However, the Coarse k-NN model had the smallest increase, with accuracy rising only by 1.1% (from 83.6% to 84.7%), suggesting that Nearest Neighbor approaches may not fully exploit the transition time feature, due to their dependence on distance-based similarity. In addition, the Wide Neural Network model demonstrated the most substantial performance enhancement, particularly in Dataset 2, where accuracy increased from 82.3% to 94.7%, highlighting the strong impact of temporal features on deep learning models. Similarly, Ensemble methods, such as Ensemble Bagged Trees, also benefited significantly from this additional feature, achieving up to 93.1% accuracy in Dataset 2. The Fine Tree and Weighted k-NN models also exhibited notable improvements, especially in Dataset 2, where their accuracy rose by approximately 11% and 12%, respectively. However, for Dataset 3 the performance gain was relatively modest, with algorithms such as Linear Discriminant Analysis and Cosine k-NN showing only a slight improvement of around 0.6-1%, indicating that the effectiveness of the transition time feature may vary depending on the spatial layout and dataset characteristics. Despite this variation, Medium Gaussian SVM consistently achieved high accuracy across all datasets, reaching 90.5% in Dataset 3. These findings emphasize that room-to-room transition time is a valuable feature that enhances Wi-Fi-based fingerprinting localization, particularly in environments where spatial movement patterns can provide meaningful contextual information for classification. Figure 8 illustrates the accuracy performance of various algorithm groups (k-NN, LDA and Logistic Regression, Neural Networks, SVM, and Tree-based) for Wi-Fi and Wi-Fi + room-to-room, evaluated on the three distinct datasets collected from the different home layouts.