Work

ML-Aided 2-D Indoor Positioning Using Energy Harvesters and Optical Detectors for Self-Powered Light-Based IoT Sensors

Year: 2025

Type: article

Abstract: Advancements in 6G and IoT sensor networks prioritize sustainability and energy efficiency, with positioning services essential for improved functionality. Light-based IoT (LIoT) systems present a promising solution by achieving energy autonomy through photovoltaic (PV) energy harvesting (EH) and enabling visible light communication (VLC) via indoor luminaries as optical access points (OAPs). This research explores the repurposing of energy harvesters at LIoT nodes and detectors at OAPs for positioning and orientation detection in energy-autonomous, battery-free, intermittently operating LIoT sensor networks. We propose potential node and OAP designs, validated by proof-of-concept prototypes, utilizing conventional machine learning (ML) and deep neural networks (DNNs) to enhance localization. Performance evaluations demonstrate 80% positioning accuracy within 12.5-cm tolerance and 68% orientation prediction accuracy. This approach allows LIoT devices to communicate, harvest energy, and determine their position and orientation using the same illumination from OAPs, underscoring the potential of this LIoT-based system as a sustainable solution for next-generation IoT sensors.

Source: IEEE Sensors Journal

DOI: 10.1109/jsen.2025.3552905

Authors: Amila Perera, Khojiakbar Botirov, Hazem Sallouha, Marcos Katz

Institutions: University of Oulu, KU Leuven

Language: English

Topic: Optical Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $2,645