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 quantification of area densities, as well as flows between specified locations, buildings or departments, classified according to the feature set. 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 proposed methods and visualization tools, we present facility utilization analysis 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. We highlight the tools’ ability to deduce people’s presences and movements and how they can provide respective insights into the test-bed hospital by investigating utilization patterns globally as well as selectively, e.g. for different user roles, daytimes, spatial granularities or focus areas.
Pervasive and Mobile Computing, 2015, Vol 16, Issue B
WiFi Monitoring; Facility Management; Spatio-temporal Data Analysis