# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
## 4 Numerical Results
### Simulation and Experiment Setup with Data Processing
Data collection spanned six days, with measurements taken in both directions during the morning and afternoon sessions. In total, 22 datasets were collected. Table 2 provides an overview of the data types, while Table 3 categorizes the datasets. Morning and afternoon data are denoted as M and A, respectively, while forward and backward directions are indicated as F and B. The numbers represent the collection day. Of the 22 datasets, 19 were used to create the training database, and 3 were reserved for testing. Each data collection session at an RP took approximately 25 s, and the entire measurement process across all 74 RPs required about 40 min, including the time taken to move the smartphone between points. This totaled approximately 15 h over five days. The fingerprinting technique employed in this study builds on the method proposed by [PERSON] et al. [9], with modifications made to the CNN model for improved performance. The process is divided into two phases, as illustrated in Figure 7:  
Offline phase:  
* RSSI data from all APs are collected at predefined RPs to create a fingerprint database representing the environment. Each RP has a unique fingerprint defined by its location and the RSSI values recorded from nearby APs. * The collected data are pre-processed and fed into a convolutional neural network (CNN) for simulation training. This process yields a trained model that incorporates the feature information of the RSSI database. Online phase: The detailed real-time localization procedure is as follows. * Real-Time RSSI Data Acquisition: The phone scans nearby WiFi signals and transmits the data to the computer via HPOWD, where the system records the MAC addresses and corresponding RSSI values. * MAC Address Matching with Database: The system retrieves pre-stored MAC addresses from the database. It aligns the collected MAC addresses with database entries to generate a table. The first row includes the reference MAC addresses from the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Database Type**} & \multirow{2}{*}{**Collection**} & \multicolumn{3}{c}{**\#of Images**} \\ \cline{3-4}  & & **Before** & **After** \\  & & **Augmentation** & **Augmentation** \\ \hline Training & 19 sets & 7008 & 427,488 \\ Test & 3 sets & 1110 & – \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Database information and augmentation size. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Morning** & **Morning** & **Afternoon** & **Afternoon** & **Number of** \\  & **Forward** & **Backward** & **Forward** & **Forward** & **Data Files** \\ \hline
0 m & MF1 & MB1, MB2 & AF1, AF2 & AB1, AB2 & 7 \\
1 m & MF2, MF3 & MB3, MB4 & AF3, AF4 & AB3, AB4 & 8 \\
2 m & MF4 & MB5, MB6 & AF5, AF6 & AB5, AB6 & 7 \\ Number of & 4 & 6 & 6 & 6 & 22 \\ Data Files & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Types of datasets. database, and the second one records the RSSI values corresponding to the matched MAC addresses. * RSSI Data Transformation to a 16 \(\times\) 16 Image: After filling in the missing entries, the RSSI values are organized into a 16 \(\times\) 16 matrix to ensure compliance with the input requirements of the Convolutional Neural Network (CNN) model. * CNN-Based Localization Prediction: The pre-trained CNN model takes the 16 \(\times\) 16 RSSI image as input, processes it, and then outputs the predicted location. To provide a clearer understanding of the transformation process of RSSI signals and the method used to achieve positioning accuracy, the procedure is divided into two phases: the offline phase and the online phase. In the offline phase, RSSI signals are collected and divided into a training set and a test set, both stored as CSV files. These files are then converted into TXT files to extract MAC address lists. The training set undergoes data augmentation to expand the dataset and improve the model's generalization capability. The augmented training set and the original test set are then used to train a CNN model.