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dc.contributor.authorCarroll-Woolery, Lannois 17:50:30 (GMT) 17:50:30 (GMT)
dc.description.abstractThe localization and counting of persons in indoor spaces is an area of extensive research. Indoor population metrics can inform energy conservation, health and safety, security, resource optimization, and location-aware services such as marketing and navigation. Building utility is impacted by the number of persons in each space, and the management of person flows into and out of building spaces is a critical consideration of space design, and the COVID-19 pandemic elevated the need to accurately measure and monitor indoor populations. Indoor populations’ size, movement and location can be ascertained by a variety of automatic means, but scalability, repeatability and cost are limiting factors. One low-cost technique is the use of wireless logs from Wi-Fi-enabled devices, which provide precise counts but inaccurate locations due to Access Points’ widely varying coverage areas. Population locations, as estimated by wireless logs, are usually defined at a floor, or building level. In this paper, I propose a generalized technique for more precise identification of indoor populations’ location, using wireless logs. It is based on the merging of connection logs with floor layout plans, to define floor zones, representing the general area(s) of wireless coverage provided by each wireless AP, including areas served by more than one AP. The combined information allows for more precise location and counting of indoor populations. This analysis could be useful across multiple functional domains, including sustainability management, resource optimization, and capacity monitoring. The technique can be implemented in any environment where there is an extensive wireless network, widespread usage of the network, and reliable data records. It is non-invasive and does not require the purchase or installation of new equipment. As a case study, we applied the technique to data from a mid-sized university. Spatial and temporal population analyses were completed using wireless logs collected over a 6-week period prior to the COVID pandemic. The logs included unique User Ids and Device Ids; The floor layout plans included the installed locations of AP devices. Facilities management records included building, floor, and room metrics. Population analyses were completed by building, room types, work weeks, and duration of wireless connections. The population estimations for size and location were compared to expected indoor populations, based on student class enrolments and employee work schedules, to gauge accuracy and utility. Linear Correlation Coefficients were calculated for measured vs. expected population counts. The results indicated that the definition of Building Floor Zones provided more accurate indoor population location values than floor-level estimates, across a variety of building types and room types. Facilities management definitions for Building Floors allowed generic description of campus spaces that could be applied to any environment with varying building usage and occupant activity. The merged data allowed the estimation of indoor populations’ size and location at various levels of aggregation: zones, floors, and buildings; and allows for comparisons of activity in similar environments in differing locations. Possible research and/or application areas include: the use of indoor spaces outside of business hours, occupancy/utility rates, and the measurement of indoor crowd densities.en
dc.publisherUniversity of Waterlooen
dc.titleLocalization and Counting of Indoor Populations on a University Campus using Wi-Fi Connection Logs and Floor Plansen
dc.typeMaster Thesisen
dc.pendingfalse Sciencesen Scienceen of Waterlooen
uws-etd.degreeMaster of Applied Scienceen
uws.contributor.advisorGolab, Lukasz
uws.contributor.affiliation1Faculty of Engineeringen

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