Transfer Learning-driven Scalability for Indoor Positioning Systems in Industrial Environments

Accurate and timely spatial-temporal data enables organizations to improve operational efficiency by optimizing production processes, monitoring worker safety, and managing resources. Indoor positioning systems (IPS) are critical for acquiring such data in environments where GNSS does not work. However, traditional IPS implementations often involve substantial effort in signal collection and system calibration, which can be challenging when production layouts change frequently or expand. This research introduces a transfer learning-enabled indoor positioning system (TLIPS) to address these challenges. TLIPS reduces the need for extensive data collection and system calibration by leveraging operational knowledge from existing environments. By applying transfer learning (TL), TLIPS enables the system to adapt and calibrate automatically in new environments, using minimal new data. This significantly reduces manual intervention and calibration time. The effectiveness of TLIPS was tested and validated through deployments in both experimental testbed (source environment) and new environment (target environment). Results demonstrated a marked reduction in calibration time and costs, highlighting the efficiency and adaptability of TLIPS. This approach offers a scalable and efficient solution for IPS deployment across diverse industrial environments, making the system more flexible and less dependent on frequent manual adjustments.