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

A Transfer Learning-Driven Methodology for Efficient and Sustainable Indoor Localization Using VLC

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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.

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