# Research on Wi-Fi Fingerprint Database Construction Method Based on Environmental Feature Awareness
## 3 Environmental Feature Sensing Model  
### Model Overview  
The environmental feature-aware regression model efficiently and accurately predicts RSSI values by incorporating systematic data preprocessing, standardization, neural network training, and prediction processes. The model takes into account various factors that influence signal propagation as features. Wi-Fi signals are affected by a range of environmental conditions during indoor propagation, particularly the physical characteristics of the space, such as room size, wall thickness, and obstacles. These features directly impact the attenuation and propagation path of RSSI values, which in turn influence the accuracy of (X, Y) location prediction using RSSI fingerprinting. For instance, the size of the room (e.g., its length and width) determines the distance over which the signal propagates. According to the free-space path loss model for wireless signal propagation, RSSI values decay exponentially with distance, meaning that larger rooms typically result in weaker signal strength. Additionally, the thickness and material composition of the walls play a crucial role in signal attenuation. Thicker walls, particularly those made of concrete or metal, cause significant signal loss, leading to a much lower signal strength when passing through multiple layers of walls compared to open spaces. We assume the initial signal strength is \(\mathbf{P}_{(\mathbf{0})}\) and the distance is \(\mathbf{d}\). The resulting signal strength, \(\mathbf{P}_{(\mathbf{d})}\), can be expressed as follows:  
\[\mathbf{P}_{(\mathbf{d})}=\mathbf{P}_{(\mathbf{0})}-\mathbf{10}\mathbf{n}\mathbf{o}\mathbf{g}_{\mathbf{10}}(\mathbf{d} )-\mathbf{L}, \tag{1}\]  
Here, \(\mathbf{n}\) represents the path loss index (which reflects the signal attenuation due to environmental factors) and accounts for additional losses caused by walls and other obstacles. This means that features such as wall thickness, material, and distance directly influence the value of D, thereby affecting the RSSI value. When the model is used to predict a user's location, it is essential to consider the impact of these environmental features on the RSSI values. The model must learn how these factors alter the RSSI signal to enhance the accuracy of its predictions. Therefore, the selection of environmental features is critical in the model's design, as it ensures that these features accurately represent the Wi-Fi signal's propagation characteristics, ultimately improving the model's prediction accuracy. By incorporating these features, the model can better capture the patterns of RSSI signal changes in different rooms, reducing localization errors and enhancing predictive performance. The main feature parameters used in this model are detailed in Table 1. The model's design optimizes each component to ensure reliability and adaptability in practical applications. The implementation of the environmental feature-aware model begins with data loading and preprocessing. This process starts by loading the dataset, defining the feature \begin{table}
\begin{tabular}{c c} \hline
**Feature Name** & **Comment** \\ \hline room\_global\_x & Horizontal coordinates of the center of the room in the global coordinate system \\ room\_global\_y & Vertical coordinates of the center of the room in the global coordinate system \\ room\_length & Room length \\ room\_width & Room width \\ room\_wall\_thickness & Room wall thickness \\ bathroom\_global\_x & Horizontal coordinates of the center of the bathroom in the global coordinate system \\ bathroom\_global\_y & Vertical coordinate of the center of the bathroom in the global coordinate system \\ bathroom\_length & Bathroom length \\ bathroom\_width & Bathroom width \\ bathroom\_wall\_thickness & Bathroom wall thickness \\ corridor\_width & Corridor width \\ AP\_x & Horizontal coordinates of the AP position in the global coordinate system \\ AP\_y & Vertical coordinate of the AP position in the global coordinate system \\ \hline \end{tabular}
\end{table} 
Table 1: Characteristic parameters.