Understanding occupancy and user behaviour through Wi-Fi-based indoor positioning

Abstract

A 30-day monitoring campaign was conducted in a university library building to investigate the usefulness of a novel Wi-Fi based indoor location system for revealing indoor occupancy patterns and related user behaviour. The system has demonstrated its effectiveness in providing occupancy information with a relatively high degree of granularity and accuracy in this study. The occupancy results revealed that the 24-hour opening policy for the library during the term time was not necessary. On the other hand, the 8-hour library-opening duration during the summer vacation could be extended to include the early evening hours to benefit user productivity. Four occupancy patterns were identified based on cluster analysis. Most users were found to belong to the short-occupancy one-time visitor type, while a minority were the long-occupancy users. The cross-correlations between various occupancy parameters were investigated. For example, the pattern of user arrival times at the library was found to be significantly correlated with their study durations. Further data analysis showed that the majority of long-occupancy users tended not to have frequent breaks with some taking no break for 4 hours. This could have implications for their health and wellbeing as well as their productivity.

Towards a smart campus: supporting campus decisions with Internet of Things applications

“…Zhang et al (2010) advise enlarging the spaces along the windows in the studied office because use of these spaces was found to be suboptimal in their IoT-based measurements. Wang and Shao (2017) write that their results revealed that the current 24-hour opening policy for the library during term time did not correlate with usage. On the other hand, the eight-hour library-opening duration during the summer holiday period could be extended to include the early evening hours to benefit user productivity.…”

Learnings from User Digital Trail Post-Occupancy Evaluation before COVID-19 for Future Workplace Analysis and Design

“…Every time user devices (laptop, mobile phone...) conduct a scan of nearby access points (looking for Wi-Fi), all access points in range receive a message containing the unique identifier of the device and a signal strength corresponding to the distance to the access point. The combination of signals received by each access point is called a fingerprint" [19,48,49,60,61].…”

“…Accuracy for indoor navigation: more accurate than Global Positioning System (GPS) or Bluetooth [48,49].…”

Critical review and research roadmap of office building energy management based on occupancy monitoring

“…Many studies tracked occupancy using Wi-Fi networks and used the results of occupancy detection for demanddriven control of building systems [42,76]. Wang and Shao [41] used Wi-Fi based indoor positioning system and created occupancy profiles based on the measured Wi-Fi devices' number and locations in a university library building.…”

“…Categorization of Research Papers Based on Different Occupancy Monitoring Systems (77 papers)Balaji et al[38]; Chen and Ahn[39]; Jain and Madamopoulos[40]; Wang and Shao[41]; Wang and Shao[42]; Wang et al[43]; Çiftler et al[44]; Wang et al[45]; Yang et al[46] PIR, and CO 2 sensors Meyn et al[47] Wired CO 2 and indoor air quality sensing network, wireless ambient sensing network Lam et al[48]; Dong et al[49] …”