Occupant counting modelling for intelligent buildings based on data from multiple WiFi sniffers. (5th October 2020)

• Record Type: Journal Article
• Authors: Wang, Ping; Zhang, Zhenya; Fang, Qiansheng; Cao, Huaqian; Chen, Si
• Is Part Of: International journal of simulation and process modelling. Volume 15:Number 4(2020)
• Page Start: 387
• Page End: 399
• Publication Date: 2020-10-05
• Subjects: occupant counting -- classification -- WiFi sniffers -- association analysis -- frequent itemsets; Management -- Computer simulation -- Periodicals; Mathematical models -- Periodicals; Operations research -- Periodicals; Simulation methods -- Periodicals; 003.05
• Languages: English
• ISSNs: 1740-2123

Abstract

Knowing the occupancy information in each room can create energy saving through intelligent control of HVAC systems. In this paper, a classification-based occupant counting method using multiple WiFi sniffers is proposed firstly to get a coarse estimation of occupancy. Then, to deal with the false negative problem, i.e., those occupants who do not carry a smartphone or if the WiFi module is not enabled and cannot be counted, a p-persistent frequent itemsets with 1-right-hand-side (RHS)-based occupant correction algorithm is further proposed to improve the occupant detection accuracy using association analysis. Finally, our methods are validated through real experiments. Results show that our classification-based occupant detection method using multiple WiFi sniffers outperforms the 1-WiFi-sniffer-based method, and the association analysis-based correction algorithm can improve the accuracy because it can see occupants in buildings that the naive WiFi-based occupant detection method cannot see, which makes it suffice to be a viable approach to occupant estimation for intelligent buildings.