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

TL;DR

This paper presents a transfer learning approach using deep neural networks to enhance VLC-based indoor localization accuracy and efficiency in industrial environments with environmental variability.

Contribution

It introduces a novel transfer learning framework that significantly improves localization accuracy, reduces energy consumption, and decreases computational time in VLC-based indoor localization.

Findings

Localization accuracy improved by 47%

Energy consumption reduced by 32%

Computational time decreased by 40%

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.