IoT-Dynamic Indoor Localization Leveraging Transfer Learning Techniques

With the rapid growth of location-based services (LBS) in the Internet of Things (IoT), fingerprint-based indoor localization has attracted attention for its high accuracy. However, environmental changes degrade signal stability, and traditional methods require frequent site surveys, leading to high labor and time costs. In response, we propose an adaptive Bluetooth Low Energy (BLE) indoor localization system that relies on an updated fingerprint to address these issues. We integrate the Domain Adaptation Localization (DALoc) method into the system. The framework combines historical data with deep transfer learning to extract features and update fingerprints based on a small amount of labeled data at a new time. To enhance the adaptability of the Received Signal Strength (RSS), we utilize historically collected RSS data to fit a K-order Gaussian mixture model (GMM). Furthermore, we assess the system’s performance using the Cramér-Rao lower bound (CRLB) to ensure reliability and robustness. The DAloc approach helps address the challenges posed by mixed and time-varying signals. We conducted multiple sets of experiments related to positioning error in the laboratory corridor, and the results demonstrate that the system’s location accuracy exceeds 70% when tested with dynamic signals, with a location error within the meter-level range.