Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86175
Title: Counting via LED sensing : inferring occupancy using lighting infrastructure
Authors: Yang, Yanbing
Luo, Jun
Hao, Jie
Pan, Sinno Jialin
Keywords: Occupancy Inference
Engineering::Computer science and engineering
Visible Light Sensing
Issue Date: 2018
Source: Yang, Y., Luo, J., Hao, J., & Pan, S. J. (2018). Counting via LED sensing : Inferring occupancy using lighting infrastructure. Pervasive and Mobile Computing, 45, 35-54. doi:10.1016/j.pmcj.2018.01.003
Series/Report no.: Pervasive and Mobile Computing
Abstract: As a key component of building management and security, occupancy inference through smart sensing has attracted a lot of research attention for nearly two decades. Nevertheless, existing solutions mostly rely on either pre-deployed infrastructures or user device participation, thus hampering their wide adoption. This paper presents CeilingSee, a dedicated occupancy inference system free of heavy infrastructure deployments and user involvements. Building upon existing LED lighting systems, CeilingSee converts part of the ceiling-mounted LED luminaires to act as sensors, sensing the variances in diffuse reflection caused by occupants. In realizing CeilingSee, we first re-design the LED driver to leverage LED’s photoelectric effect so as to transform a light emitter to a light sensor. In order to produce accurate occupancy inference, we then engineer efficient learning algorithms to fuse sensing information gathered by multiple LED luminaires. We build a testbed covering a 30 m2 office area; extensive experiments show that CeilingSee is able to achieve very high accuracy in occupancy inference.
URI: https://hdl.handle.net/10356/86175
http://hdl.handle.net/10220/49263
ISSN: 1574-1192
DOI: 10.1016/j.pmcj.2018.01.003
Rights: © 2018 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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