Modeling Classroom Occupancy using Data of WiFi Infrastructure in a University Campus

TL;DR

This paper develops machine learning models to estimate classroom occupancy using WiFi infrastructure data, addressing challenges of indirect measurement in dense campus environments and achieving high accuracy comparable to sensor-based methods.

Contribution

It introduces a novel approach combining unsupervised clustering and machine learning to map WiFi access points to classrooms and estimate occupancy accurately.

Findings

Achieved 84.6% accuracy in AP-to-classroom mapping.

Estimated occupancy with a sMAPE of 13.10%.

Provided insights into WiFi coverage and user behavior in campus environments.

Abstract

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%.