# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
## 4 Numerical Results  
### Simulation and Experiment Setup with Data Processing  
The data collection, simulation tests, and experimental tests were conducted on the 7 th floor of the New Engineering Building at Dongguk University. This environment facilitated the generation of an RSSI fingerprint dataset. As shown in Figure 5, 74 reference points (RPs) were designated, with a 2-m interval between each RP. On the hardware side, a Samsung SHV-E310K smartphone (Samsung Electronics, Suwon, Republic of Korea), a MAP200N high-performance wireless device (HPOWD) (JMP SYSTEM, Korea), and a Dell Alienware Model P31E (Dell Inc., Round Rock, TX, USA) were used. During the data collection and processing phase, the smartphone collected RSSI signals. These signals were then transmitted to the computer through the HPOWD for storage. The computer then performed database construction, data preprocessing, data augmentation, and CNN training. In the positioning phase, the smartphone was placed on a height-adjustable stand. This was performed to ensure accurate and precise positioning. Subsequently, the smartphone collected RSSI signals in real time. After that, it transmitted these signals to the computer via the HPOWD. Once the computer received the data, it processed the data and finally displayed the positioning results. On the software side, a custom-developed mobile application facilitated RSSI data collection and file integration, while a Python script on the server handled the RSSI signal files sent from the smartphone. The overall data collection setup is illustrated in Figure 6. Specifically, the smartphone processed the collected RSSI signals and transmitted them to the server in text file (TXT) format. The files stored the RSSI signal and MAC address in the format REF000000 XX, where XX represents the RP number. \begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Our CNN Models** \\ \hline Input Size & 16 \(\times\) 16 \\ Number of Convolutional Layers & 3 \\ Filter Size & 3 \\ Number of Feature Maps & 32–128 \\ Pooling Size & 2 \\ Learning Rate & 0.001 \\ Batch Size & 200 \\ Stride & 1 \\ Number of FC Layers & 2 \\ Total Weights & 4.50 million \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameter settings for the CNN model. The simulation and experimental tests necessitated a significant investment of time. The simulation phase, which spanned approximately 35 days, involved the execution of ten sections, each undergoing 200 epochs. Each epoch took around 25 min, resulting in a total of 84 h per section. Given that the CNN control section was simulated only once, the total time for all sections amounted to approximately 840 h, which was equivalent to 35 days. For the experimental tests, the best-performing section from the simulations was selected for further evaluation. The best-performing model refers to the one that achieved the highest accuracy and lowest loss during simulation processes. This phase included four sections, with signals collected five times at each of the 74 RPs, resulting in 370 data points. The results were analyzed to compute the final average outcome. Each section required 3 h to complete, summing up to 12 h for all experimental tests. In total, the combined time for simulations and experiments amounted to 36 days: 35 days for simulation and 12 h for experimental testing. To construct the RSSI database, data collection was performed five times at each RP. Each measurement included the timestamp, RSSI values from all visible APs, their  
Figure 5: Indoor environment radio map: 74 reference points (2 m \(\times\) 2 m each). Figure 6: Illustration of RSSI data collection. corresponding MAC addresses, and associated labels. Data were collected in various orientations (forward and backward) and at different times of the day (morning and afternoon). The procedure was as follows: a smartphone was positioned at a fixed height at each RP and remained stationary during measurements.