## 3 Experiment  
### Experimental Environment  
We analyze the performance of the proposed techniques using publicly available Wi-Fi fingerprint datasets. The dataset used is from a 2019 paper [24]. The dataset used in this paper contains RSSI, latitude, longitude, and layer information. Specifically, latitude and longitude data were calculated as datapoint locations. The training dataset includes 3852 data from the 0 th floor and 3323 data from the 1 st floor, with a total of 489 indoor positioning points used. The dataset also contains 443 unique APs. However, in this paper, we only used data from floor 0 because we do not consider interfloor movement. Considering only layer 0 data, the number of unique APs was found to be 173. The following hyperparameters are used to calculate the movement paths according to the proposed method. Table 1 lists the hyperparameters required for merging BBs. Here, BBi size refers to the size of the initial BB, m_factor represents the multiplier used for expanding the BB when there are no neighboring BBs around it, and max_factor is the maximum size for combining and expanding the BB, which cannot exceed 5 m. The IoU Threshold is 0. Non-overlapping BBs cannot be merged with neighboring BBs, only expanded. Table 2 presents the hyperparameters required to divide cells, where the minimum size of the divided cell is 5 m and the maximum number of BBs contained in the cell is limited to 2. If this limit is exceeded, the cell is divided. The information of the dataset needed for training is shown in Table 3, where the units are counts. Use Algorithm 5 to visit cells along the movement path from a random cell and collect RSSI data for each cell. The RSSI data from the visited cell is gathered by splitting it into train and test datasets. The last visited cell is numbered Y. Based on this data, we trained a model for the classification problem, utilizing a single slot on an Nvidia DGX A100 as the training environment. Table 4 displays the range of hyperparameters we employed for training, subsequently serving as the search space for the Bayesian optimizer. The Bayesian optimizer is a method that predicts the optimal values of hyperparameters based on a prior probability distribution, which was used to find the optimal hyperparameters. The optimal hyperparameters calculated from the experiments are summarized in Table 5.  
The initial data distribution before training the LSTM on the Wi-Fi fingerprint data used in the experiment is shown in Figure 2. This shows the distribution of data based on the Wi-Fi signal alone, regardless of the indoor structure. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hyper Parameter** & **Data Point** & **APs** & **Path** & **Training Data** & **Test Data** \\ \hline Value & 3852 & 173 & 5 & 30,000 & 5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameters for the dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hyper Parameter** & **Learning Rate** & **Dropout** & **LSTM Length** & **Hidden Cell** & **Epoch** &
\begin{tabular}{c} **RSSI** \\ **Normalize** \\ \end{tabular} & **Batch Size** \\ \hline value & 0.00001–0.01 & 0.0–0.4 & [2,3,4,5,6,7] & [64,128,256,512,1024] & 50–5000 & [True, False] & [64,128,256,512,1024] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Range of hyperparameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hyper Parameter** & **Data Point** & **APs** & **Path** & **Training Data** & **Test Data** \\ \hline Value & 3852 & 173 & 5 & 30,000 & 5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Range of hyperparameters. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hyper Parameter** & **Learning Rate** & **Dropout** & **LSTM Length** & **Hidden Cell** & **Epoch** &
\begin{tabular}{c} **RSSI** \\ **Normalize** \\ \end{tabular} & **Batch Size** \\ \hline value & 0.00001–0.01 & 0.0–0.4 & [2,3,4,5,6,7] & [64,128,256,512,1024] & 50–5000 & [True, False] & [64,128,256,512,1024] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Best Model in Learning.