Research on Wi-Fi Fingerprint Database Construction Method Based on Environmental Feature Awareness

Indoor localization technology is becoming increasingly widespread, but traditional methods for constructing Wi-Fi fingerprint databases face significant challenges, particularly in large, multi-room environments. These methods often suffer from low efficiency and high costs associated with manual data collection. To address these issues, various approaches like crowdsourcing and sparse collection have been introduced, but they still struggle with limitations such as inadequate data accuracy and uneven distribution. In this paper, we present a novel method for constructing Wi-Fi fingerprint databases based on environmental feature awareness. By leveraging deep learning to analyze the relationship between environmental features and Wi-Fi signal strength, our method enables faster and more efficient database construction. Experimental results demonstrate that our environmental feature-aware model significantly outperforms existing methods in prediction accuracy, greatly enhancing both the efficiency and accuracy of Wi-Fi fingerprint database construction. This approach also reduces the need for manual intervention and improves generalization capabilities. Our method proves to be highly practical and adaptable, especially in large-scale structures like nursing homes. It holds a substantial potential for broader application in extensive indoor environments, offering considerable value for widespread adoption.