TY - JOUR
T1 - Centimeter-Level Indoor Visible Light Positioning
AU - Zhu, Ran
AU - Van Den Abeele, Maxim
AU - Beysens, Jona
AU - Yang, Jie
AU - Wang, Qing
PY - 2024
Y1 - 2024
N2 - Visible light positioning (VLP) based on the received signal strength (RSS) can leverage a dense deployment of LEDs in future lighting infrastructure to provide accurate and energy-efficient indoor positioning. However, its positioning accuracy heavily depends on the density of collected fingerprints, which is labor-intensive. In this work, we propose a data pre-processing method, including data cleaning and data augmentation, to construct reliable and dense fingerprint samples, thereby alleviating the impact of noisy samples as well as reducing labor intensity. Extensive experiments demonstrate that our proposed method achieves an average positioning error of 1.7 cm, utilizing a sparse dataset that reduces the fingerprint collection effort by 98 percent. Running a tinyML-based model for VLP on the Arduino Nano microcontroller, we also show the possibilities for deploying RSS fingerprint-based VLP systems on resource-constrained embedded devices for real-world applications.
AB - Visible light positioning (VLP) based on the received signal strength (RSS) can leverage a dense deployment of LEDs in future lighting infrastructure to provide accurate and energy-efficient indoor positioning. However, its positioning accuracy heavily depends on the density of collected fingerprints, which is labor-intensive. In this work, we propose a data pre-processing method, including data cleaning and data augmentation, to construct reliable and dense fingerprint samples, thereby alleviating the impact of noisy samples as well as reducing labor intensity. Extensive experiments demonstrate that our proposed method achieves an average positioning error of 1.7 cm, utilizing a sparse dataset that reduces the fingerprint collection effort by 98 percent. Running a tinyML-based model for VLP on the Arduino Nano microcontroller, we also show the possibilities for deploying RSS fingerprint-based VLP systems on resource-constrained embedded devices for real-world applications.
UR - http://www.scopus.com/inward/record.url?scp=85179779242&partnerID=8YFLogxK
U2 - 10.1109/MCOM.002.2300296
DO - 10.1109/MCOM.002.2300296
M3 - Article
AN - SCOPUS:85179779242
SN - 0163-6804
VL - 62
SP - 48
EP - 53
JO - IEEE Communications Magazine
JF - IEEE Communications Magazine
IS - 3
ER -