  Universities worldwide are experiencing a surge in enrollments, therefore
campus estate managers are seeking continuous data on attendance patterns to
optimize the usage of classroom space. As a result, there is an increasing
trend to measure classrooms attendance by employing various sensing
technologies, among which pervasive WiFi infrastructure is seen as a low cost
method. In a dense campus environment, the number of connected WiFi users does
not well estimate room occupancy since connection counts are polluted by
adjoining rooms, outdoor walkways, and network load balancing.
  In this paper, we develop machine learning based models to infer classroom
occupancy from WiFi sensing infrastructure. Our contributions are three-fold:
(1) We analyze metadata from a dense and dynamic wireless network comprising of
thousands of access points (APs) to draw insights into coverage of APs,
behavior of WiFi connected users, and challenges of estimating room occupancy;
(2) We propose a method to automatically map APs to classrooms using
unsupervised clustering algorithms; and (3) We model classroom occupancy using
a combination of classification and regression methods of varying algorithms.
We achieve 84.6% accuracy in mapping APs to classrooms while the accuracy of
our estimation for room occupancy is comparable to beam counter sensors with a
symmetric Mean Absolute Percentage Error (sMAPE) of 13.10%.
