{"ID":2899126,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.01575","arxiv_id":"2507.01575","title":"Transfer Learning for VLC-based indoor Localization: Addressing Environmental Variability","abstract":"Accurate indoor localization is crucial in industrial environments. Visible Light Communication (VLC) has emerged as a promising solution, offering high accuracy, energy efficiency, and minimal electromagnetic interference. However, VLC-based indoor localization faces challenges due to environmental variability, such as lighting fluctuations and obstacles. To address these challenges, we propose a Transfer Learning (TL)-based approach for VLC-based indoor localization. Using real-world data collected at a BOSCH factory, the TL framework integrates a deep neural network (DNN) to improve localization accuracy by 47\\%, reduce energy consumption by 32\\%, and decrease computational time by 40\\% compared to the conventional models. The proposed solution is highly adaptable under varying environmental conditions and achieves similar accuracy with only 30\\% of the dataset, making it a cost-efficient and scalable option for industrial applications in Industry 4.0.","short_abstract":"Accurate indoor localization is crucial in industrial environments. Visible Light Communication (VLC) has emerged as a promising solution, offering high accuracy, energy efficiency, and minimal electromagnetic interference. However, VLC-based indoor localization faces challenges due to environmental variability, such a...","url_abs":"https://arxiv.org/abs/2507.01575","url_pdf":"https://arxiv.org/pdf/2507.01575v1","authors":"[\"Masood Jan\",\"Wafa Njima\",\"Xun Zhang\",\"Alexander Artemenko\"]","published":"2025-07-02T10:51:38Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.LG\"]","methods":"[]","has_code":false}