# Transfer Learning for VLC-based indoor Localization: Addressing Environmental Variability

arXiv: https://arxiv.org/abs/2507.01575 · HTML: https://mlsanity.com/paper/2507.01575/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 2 of 2)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2401.13893/) (2024)

## Abstract (arXiv)

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.