Paper Info
Title
An Edge Computing Approach to Explore Indoor Environmental Sensor Data for Occupancy Measurement in Office Spaces
Abstract
Human occupancy measurement has become a topic of increasing interest in the past few years, due to the important role it plays in controlling a number of demand-driven applications like smart lighting and smart heating, as well as improving the energy efficiency of these applications in a broader sense. Office occupancy monitoring in commercial buildings can yield huge savings and improvements in terms of thermal, visual, and air quality. However, this is often impeded due to the lack of fine-grained occupancy information. This paper explores the use of low-priced environmental (temperature and humidity) sensor data for measuring occupancy in an office space. The idea behind this work is to leverage the variation divergence between humidity and temperature caused by human presence. We used a Raspberry Pi with a daughterboard called Sense Hat, which is equipped with the environmental sensors used in this study. The results are compared with occupancy data obtained from camera feeds in order to assess the effectiveness and the accuracy of the combined occupancy measurements, and show up to 87% accuracy.
Year
DOI
Venue
2018
10.1109/ISC2.2018.8656753
2018 IEEE International Smart Cities Conference (ISC2)
Keywords
Field
DocType
Buildings,Cameras,Monitoring,Temperature sensors,Humidity,Cloud computing,Privacy
Environmental sensor,Edge computing,Smart lighting,Efficient energy use,Computer science,Real-time computing,Air quality index,Occupancy,Daughterboard,Cloud computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-5959-5
1
0.36
References 
Authors
0
6
Name
Order
Citations
PageRank
Sofiane Zemouri1182.39
Damien Magoni224030.36
Ayoub Zemouri310.36
Yiannis Gkoufas4175.96
Kostas Katrinis510219.41
John Murphy67510.07