| Summary: | 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 classroom 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. This paper develops machine learning-based models, including unsupervised clustering and a combination of classification and regression algorithms, 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, the behavior of WiFi-connected users, and challenges of estimating room occupancy; (2) We propose a method to automatically map APs to classrooms and evaluate K-means, Expectation-Maximization (EM-GMM) and Hierarchical Clustering (HC) algorithms; and (3) We model classroom occupancy and evaluate varying algorithms, namely Logistic Regression, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Linear Regression (LR) and Support Vector Regression (SVR). We achieve 84.6% accuracy in mapping APs to classrooms, while our estimation for room occupancy (with symmetric Mean Absolute Percentage Error (sMAPE) of 13.10%) is comparable to beam counter sensors. IEEE
|
|---|
