Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2022

Abstract

The development of the Internet of Things (IoT) opens the doors for innovative solutions in indoor positioning systems. Recently, light-based positioning has attracted much attention due to the dense and pervasive nature of light sources (e.g., Light-emitting Diode lighting) in indoor environments. Nevertheless, most existing solutions necessitate carrying a high-end phone at hand in a specific orientation to detect the light intensity with the phone's light sensing capability (i.e., light sensor or camera). This limits the ease of deployment of these solutions and leads to drainage of the phone battery. We propose PVDeepLoc, a device-free light-based indoor localization system that passively leverages photovoltaic currents generated by the solar cells powering various digital objects distributed in the environment. The basic principle is that the location of the human interferes with the lighting received by the solar cells, thus producing a location fingerprint on the generated photocurrents. These fingerprints are leveraged to train a deep learning model for localization purposes. PVDeepLoc incorporates different regularization techniques to improve the deep model's generalization and robustness against noise and interference. Results show that PVDeepLoc can localize at sub-meter accuracy for typical indoor lighting conditions. This highlights the promise of the proposed system for enabling device-free light-based localization systems.

Keywords

solar panels, deep learning, indoor localization, device-free localization

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, Pisa, Italy, March 21-25

First Page

1

Last Page

5

ISBN

9781665416481

Identifier

10.1109/PerComWorkshops53856.2022.9767256

Publisher

IEEE

City or Country

Pisa, Italy

Additional URL

http://doi.org/10.1109/PerComWorkshops53856.2022.9767256

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