# Research on Wi-Fi Fingerprint Database Construction Method Based on Environmental Feature Awareness
## 5 Conclusions  
In this paper, we propose an environmental feature-aware Wi-Fi fingerprint library construction method tailored for large, similar building structures. This method addresses the inefficiency of traditional manual Wi-Fi fingerprint collection. By introducing an environment feature-aware model, we demonstrate how deep learning techniques can be leveraged to predict indoor Wi-Fi signal strength distribution, enabling fast and efficient Wi-Fi fingerprint library construction.  
We conducted comparison experiments with a convolutional neural network (CNN), a recurrent neural network (RNN), and a deep convolutional neural network (AlexNet). The results show that the environmental feature-aware model excels in both prediction accuracy and stability, with the predicted values closely aligning with the actual values, particularly at peaks and valleys, outperforming the other models significantly. The application of deep learning neural networks greatly reduces the workload and time required for manual fingerprint collection. The experimental results confirm that the fingerprint library construction method based on environmental feature perception offers significant advantages in efficiency and accuracy, making it well suited to rapid fingerprint library construction across large areas with similar building structures.  
The Wi-Fi fingerprinting method based on environmental feature sensing proposed in this paper shows significant advantages in enhancing the efficiency and accuracy of indoor localization systems. However, there are several areas where future research can be extended and optimized. First, integrating other sensor technologies (e.g., Bluetooth, infrared, or ultra-wideband) with Wi-Fi signals could be explored to create a multimodal indoor localization system, thereby improving the system's robustness and accuracy in complex environments. Second, with the ongoing advancements in machine learning and deep learning, more efficient model architectures can be investigated, such as graph neural networks (GNNs) or self-supervised learning, to further optimize the performance and computational efficiency of positioning algorithms. Additionally, system adaptability is a crucial direction for future research--especially in environments with significant dynamic changes. The ability to quickly update the fingerprint database and perform self-correction will be a key challenge. Finally, with the rise of next-generation wireless communication technologies like 5G and 6G, the application of these technologies in indoor localization will also become a research hotspot. Future efforts will promote the broader application of indoor positioning technologies, particularly in smart cities, intelligent buildings, and health monitoring, providing more accurate and convenient localization services for everyday life.  
Validation, Z.Z.; Writing--original draft, T.Y.; Writing--review and editing, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Research Funds for Undergraduate Universities in Liaoning Province (SYLUGXTD07).  
Data are contained within the article.  
The authors declare no conflicts of interest.