# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
## 3 Proposed LBN-Aided Positioning Scheme
### CNN Model and Data Processing  
This section details the preprocessing of collected RSSI data and its use as input for the CNN model. Initially, the RSSI data are collected and transmitted to the server in a.txt file format. It is then converted into a comma-separated values (CSV) file, which serves as the input for the CNN model. As shown in Figure 3, the CSV file contains location-specific information, including the media access control (MAC) addresses of APs, their corresponding RSSI values, and the quantities recorded at each location. The MAC address information is represented by the blue box in the CSV file, with a maximum capacity of 256 MAC addresses. Each CSV file consists of 257 columns, where columns 1 to 256 represent RSSI values, and the 257 th column indicates the category (e.g., location label). There are 74 distinct categories in total. Each row contains 256 RSSI values, corresponding to signals received from various WiFi sources at a specific RP.  
Figure 4 provides a detailed overview of the CNN model architecture used in this study. The model is structured into five layers as follows:  
* Input Layer (First Layer): Accepts a 16 \(\times\) 16 \(\times\) 1 grayscale image, applies a Rectified Linear Unit (ReLU) activation function, LBN, and implements a dropout rate of 0.5.
* Second Layer: Contains 16 \(\times\) 16 convolutional filters, ReLU activation, an 8 \(\times\) 8 max-pooling layer, and a dropout rate of 0.5, with a total of 18,496 parameters.
* Third Layer: Includes 8 \(\times\) 8 convolutional filters, ReLU activation, a 4 \(\times\) 4 max-pooling layer, and a dropout rate of 0.5, with 73,856 parameters.
* Fourth Layer: A fully connected (FC) layer with 2048 nodes, resulting in 2,098,176 parameters.
* Fifth Layer: Another FC layer with 1024 nodes, totaling 76,875 parameters. The outputs are calculated using a softmax layer with 74 nodes, corresponding to the total number of RPs.  
Figure 4: Architecture of the CNN model.  
Figure 3: Example of the comma-separated values (CSV) file format used as input for the CNN model [9].  
The model uses a learning rate of 0.001 and has a total of 2,267,851 parameters. LBN is applied at every layer where traditional batch normalization is typically used. This technique normalizes local distributions to adapt to the spatial heterogeneity of RSSI data, enhancing feature sensitivity and improving robustness across varied inputs. A summary of all parameter settings is provided in Table 1. The primary objective of data augmentation is to improve the model's generalization to unseen data. The strategy employed is based on the method proposed by [PERSON] et al. [9], designed to generate additional RPs and expand the dataset significantly. Each row of the CSV file, containing 256 RSSI values, is transformed into a 16 \(\times\) 16 grayscale image for processing in the CNN model. The leftmost image in Figure 4 illustrates an example of a generated image. In these images, darker regions represent stronger RSSI signals, while lighter regions indicate weaker signals. If the RSSI value is 0, no brightness is generated. The original dataset comprises 7008 images for the training set. After applying data augmentation, this number increases to 427,488 images. The test set includes 1110 images.