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The paper introduces a transfer learning framework for VLC-based indoor localization that improves cross-area robustness and reduces data needs. Using real measurements from a Bosch facility, fine‑tuning a deep neural network with only 40% of target data recovers 97% of full‑data performance. The authors also propose a Transfer Efficiency Score (TES) to select the best source model, achieving localization errors as low as 40.9 cm and a 3.28 cm mean error on an unseen area.
The rapid expansion of the Internet of Things enables seamless collaboration among connected devices, making indoor localization a critical component of industrial automation. However, conventional lo...
The paper presents a transfer learning–driven approach for indoor localization using visible light communication (VLC) that aims to reduce energy consumption and improve scalability. By applying transfer learning, the method addresses environmental variability such as lighting changes and obstacles, lowering computational overhead compared to conventional techniques. Experiments on a BOSCH factory dataset show a 47% increase in localization accuracy and up to 67.5% model efficiency under high‑noise conditions, outperforming non‑TL models.
arXiv:2608. 09285v1 Announce Type: cross Abstract: Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes.
arXiv:2606. 01899v1 Announce Type: cross Abstract: Wireless localization is a fundamental capability of sixth-generation (6G) networks.
arXiv:2407. 13303v2 Announce Type: replace Abstract: Conventional large-scale indoor localization based on Wi-Fi RSSI fingerprinting faces issues of time-consuming and labor-intensive labeled data collection, limited generalization of a model trained under a supervised learning (SL) framework due to its inability to leverage unlabeled data, and model performance degradation in dynamic scenarios with environmental variations.