Hee-Jun Lee  Sang-Hwa Chung  Jeongbae Park

10.5626/JOK.2025.52.9.795

Indoor Localization data augmentation RSSI fingerprints industrial IoT (IIoT) Machine Learning

The smartification of factories is progressing rapidly in many manufacturing environments. To digitalize maintenance processes, we have developed an innovative smart E-Ink tag system. A key feature of this system is its RSSI-based BLE smart tag localization function, which determines the real-time location of smart tags attached to process logistics. However, gathering sufficient RSSI fingerprint data to establish the system presents several challenges, including interference from operational equipment, workers, metal structures, and other obstacles, which can consume considerable time and resources. This study aims to enhance the limited RSSI fingerprint data collected from a bolt manufacturing factory and improve the localization performance of smart tags. We compare and analyze the errors and effectiveness of various data augmentation techniques alongside machine learning-based localization methods, including WkNN, Random Forest, Multilayer Perceptron, and LSTM.