Visible Light Positioning with TinyML

Trzykowski, J.A.

Visible Light Positioning with TinyML

Improving Data Quality and Reducing Data Collection Effort

Bachelor thesis (2024)

Authors

J.A. Trzykowski Electrical Engineering, Mathematics and Computer Science

Contributors

Q. Wang Embedded Systems - (mentor)

R. Zhu Embedded Systems - (mentor)

J.A. Pouwelse Data-Intensive Systems - (graduation committee member)

Faculty

Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science

Machine Learning Data Science Data Augmentation TinyML LED Visible Light Communication RSS Data Cleaning VLP Visible Light Positioning

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Published Date

25-06-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Visible light positioning (VLP) systems enable indoor positioning through a deployment of light-emitting diodes (LEDs) as transmitters and photodiodes (PDs) as receivers. A promising approach in VLP involves recording the received signal strength (RSS) to construct fingerprint samples for later use in positioning. However, achieving high accuracy demands a labor-intensive data collection process.

In this study, we propose improvements to a data cleaning and augmentation pipeline. Our improvements focus on preserving more source data during cleaning and data-based LED position estimation for more reliable data augmentation. Experimental results show that our approach maintains comparable positioning accuracy while reducing data collection efforts by over 99%. Furthermore, we conduct experiments to investigate the impact of spatially irregular data collection strategies on positioning accuracy. Finally, we deploy a machine learning model on a microcontroller to demonstrate the practical feasibility of our proposed methods.

Files

Research_paper_finalv2.pdf

(pdf | 10.1 Mb)

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