Analysis methods for extracting knowledge from large-scale WiFi monitoring to inform building facility planning

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Abstract
The optimization of logistics in large building complexes with many resources, such as hospitals, require realistic facility management and planning. Current planning practices rely foremost on manual observations or coarse unverified assumptions and therefore do not properly scale or provide realistic data to inform facility planning. In this paper, we propose analysis methods to extract knowledge from large sets of network collected WiFi traces to better inform facility management and planning in large building complexes. The analysis methods, which build on a rich set of temporal and spatial features, include methods for noise removal, e.g., labeling of beyond building-perimeter devices, and methods for quantification of area densities and flows, e.g., building enter and exit events, and for classifying the behavior of people, e.g., into user roles such as visitor, hospitalized or employee. Spatio-temporal visualization tools built on top of these methods enable planners to inspect and explore extracted information to inform facility-planning activities. To evaluate the methods, we present results for a large hospital complex covering more than 10 hectares. The evaluation is based on WiFi traces collected in the hospital's WiFi infrastructure over two weeks observing around 18000 different devices recording more than a billion individual WiFi measurements. For the presented analysis methods we present quantitative performance results, e.g., demonstrating over 95% accuracy for correct noise removal of beyond building perimeter devices. We furthermore present detailed statistics from our analysis regarding people's presence, movement and roles, and example types of visualizations that both highlight their potential as inspection tools for planners and provide interesting insights into the test-bed hospital.

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