VLocSense: Integrated VLC System for Indoor Passive Localization and Human Sensing

The demand for accurate and real-time indoor localization and human sensing is rising with the development of smart environments, with applications in security and smart homes. Effective systems enhance safety, energy efficiency, and user experience by leveraging existing infrastructure, reducing deployment costs, and integrating seamlessly. Traditional methods rely on dedicated hardware, while communication or lighting infrastructure can provide dual-purpose solutions. This research focuses on Visible Light Communication (VLC) technology, which uses visible light for data transmission. Our VLC system utilizes existing lighting infrastructure to transmit data, providing localization and human sensing functionalities. The system design strategically incorporates specific VLC transmitters and receivers to enhance sensing performance. The collected data is processed using advanced algorithms and machine learning models, ensuring robust, real-time, and cost-effective localization with an accuracy of 96.6% and human activity recognition with an accuracy of 98.3%. This multi-functionality system demonstrates the potential for VLC in healthcare monitoring and home automation applications.

Authors

Institutions

Publication Details

Published
2024-12-04
DOI
https://doi.org/10.1145/3636534.3694729
Citations
4
Primary Topic
Optical Wireless Communication Technologies
Type
article
Field-Weighted Citation Impact
0.83

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

VLocSense: Integrated VLC System for Indoor Passive Localization and Human Sensing

Changshuo Ge, Xiaojun Liang, Jiarong Li, Wenbo Ding et al.
4 citations
Optical Wireless Communication Technologies
0.83
article

VLocSense: Integrated VLC System for Indoor Passive Localization and Human Sensing

Changshuo Ge, Xiaojun Liang, Jiarong Li, Wenbo Ding, Weihua Gui, Chaobo Zhang, Jiahui Gong, Chihan Xu
article en
4 citations

Abstract

The demand for accurate and real-time indoor localization and human sensing is rising with the development of smart environments, with applications in security and smart homes. Effective systems enhance safety, energy efficiency, and user experience by leveraging existing infrastructure, reducing deployment costs, and integrating seamlessly. Traditional methods rely on dedicated hardware, while communication or lighting infrastructure can provide dual-purpose solutions. This research focuses on Visible Light Communication (VLC) technology, which uses visible light for data transmission. Our VLC system utilizes existing lighting infrastructure to transmit data, providing localization and human sensing functionalities. The system design strategically incorporates specific VLC transmitters and receivers to enhance sensing performance. The collected data is processed using advanced algorithms and machine learning models, ensuring robust, real-time, and cost-effective localization with an accuracy of 96.6% and human activity recognition with an accuracy of 98.3%. This multi-functionality system demonstrates the potential for VLC in healthcare monitoring and home automation applications.

Central South University (CN), University Town of Shenzhen (CN), Tsinghua–Berkeley Shenzhen Institute (CN), Peng Cheng Laboratory (CN), Tsinghua University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 26%
Optical Wireless Communication Technologies
0.83
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

VLocSense: Integrated VLC System for Indoor Passive Localization and Human Sensing — Changshuo Ge, Xiaojun Liang, et al. · (2024) | TGRS Research Map | TGRS