# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 4 Experimental Results and Discussions
_Medium Gaussian SVM_, with training complexity of \(O(N^{2}\cdot F)\approx O(2700^{2}\cdot 63)\), was one of the most computationally expensive algorithms, in terms of training. Its testing complexity, similar to Linear SVM, was \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ML Algorithm** & **Dataset 3** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{2}{*}{Fine Tree} & Wi-Fi & 81.1 & 81 & 81 \\  & Wi-Fi + RR & 83.9 & 83.8 & 83.8 \\ \hline \multirow{2}{*}{Linear Discriminant} & Wi-Fi & 88.9 & 88.8 & 88.8 \\  & Wi-Fi + RR & 89.5 & 89.4 & 89.4 \\ \hline \multirow{2}{*}{Cosine k-NN} & Wi-Fi & 88.7 & 88.7 & 88.7 \\  & Wi-Fi + RR & 89.7 & 89.6 & 89.6 \\ \hline \multirow{2}{*}{Medium Gaussian SVM} & Wi-Fi & 89.9 & 89.9 & 89.9 \\  & Wi-Fi + RR & 90.5 & 90.5 & 90.5 \\ \hline \multirow{2}{*}{Wide Neural Network} & Wi-Fi & 78.2 & 78.1 & 78.1 \\  & Wi-Fi + RR & 85.6 & 85.5 & **85.5** \\ \hline \multirow{2}{*}{Ensemble Bagged Trees} & Wi-Fi & 89.2 & 89.1 & 89.1 \\  & Wi-Fi + RR & 88.6 & 88.5 & 88.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Precision, Recall, and F1 scores for Wi-Fi and Wi-Fi + room-to-room transition time (Wi-Fi + RR) for Dataset 3. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**ML Algorithm** & **Dataset 2** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{2}{*}{Linear SVM} & Wi-Fi & 90.5 & 81.8 & 85.9 \\  & Wi-Fi + RR & 90.7 & 89.5 & 90.1 \\ \hline \multirow{2}{*}{Wide Neural Network} & Wi-Fi & 91 & 82.3 & 83 \\  & Wi-Fi + RR & 94.6 & 94.6 & 94.6 \\ \hline \multirow{2}{*}{Ensemble Bagged Trees} & Wi-Fi & 90.7 & 82 & 86.1 \\  & Wi-Fi + RR & 93 & 92.8 & 92.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._\(O(F\cdot M)\). While it achieved strong performance improvements with Wi-Fi + RR, its high training cost makes it unsuitable for large datasets or real-time applications. _Wide Neural Network_, with the highest training and testing complexities among all the models, had complexity of \(O(I\cdot N\cdot(F\cdot W+W^{2}\cdot L))\approx O(100\cdot 2700\cdot(63\cdot 10 +10^{2}\cdot 3))\approx O(2.51\cdot 10^{8})\) for training and \(O(F\cdot W+W^{2}\cdot L)\approx O(63\cdot 10+10^{2}\cdot 3)\approx O(930)\) for testing. Despite its computational demands, it showed the highest improvement across all the performance metrics, making it ideal for accuracy-focused applications where computational resources are available. _Ensemble Bagged Trees_, with training complexity of \(O(B\cdot N\cdot F\cdot D)\approx O(100\cdot 2700\cdot 63\cdot 4)\approx O(6.8 \cdot 10^{7})\), balanced high training cost with low testing complexity (\(O(B\cdot D)\approx O(100\cdot 4)\approx O(400)\)). It exhibited one of the highest accuracy improvements with Wi-Fi + RR, making it a strong candidate for practical deployment where high training cost is acceptable. _Linear Discriminant Analysis (LDA)_, with training complexity of \(O(F^{2}+F\cdot N)\), was one of the fastest models, in terms of both training and testing (\(O(F)\approx O(63)\)). While its accuracy improvement was limited, it is highly efficient for real-time applications. Overall, the new features in the Wi-Fi + RR data led to more accurate predictions, and the more complex algorithms performed better with the increase in data. This improvement suggests that incorporating additional spatial-temporal features, such as room-to-room transition time, enhances the algorithms' ability to model human mobility patterns within indoor environments. Building upon these findings, this study further investigated the generalizability of the room-to-room transition time feature by collecting data from three different environments with distinct layouts and from individuals of varying genders and ages. Despite the variability in walking speeds and room-to-room distances, the experimental results demonstrate that the ML models consistently achieved higher accuracy when trained with Wi-Fi + RR data across all the datasets.