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arXiv Machine Learning By Masood Jan, Wafa Njima, Xun Zhang, Alexander Artemenko

Cross-Area Transfer Learning for VLC-based Indoor Localization with a Transfer Efficiency Score

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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.

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