Experiences With IoT and AI in a Smart Campus for Optimizing Classroom Usage

Abstract

Increasing demand for university education is putting pressure on campuses to make better use of their real-estate resources. Evidence indicates that enrollments are rising, yet attendance is falling due to diverse demands on student time and easy access to online content. This paper outlines our efforts to address classroom under-utilization in a real university campus arising from the gap between enrollment and attendance. We do so by instrumenting classrooms with Internet of Things (IoT) sensors to measure real-time usage, using AI to predict attendance, and performing optimal allocation of rooms to courses so as to minimize space wastage. Our first contribution undertakes an evaluation of several IoT sensing approaches for measuring class occupancy, and comparing them in terms of cost, accuracy, privacy, and ease of deployment/operation. Our second contribution instruments nine lecture halls of varying capacity across campus, collects and cleans live occupancy data spanning about 250 courses over two sessions, and draws insights into attendance patterns, including identification of canceled lectures and class tests, while also releasing our data openly to the public. Our third contribution is to use AI techniques for predicting classroom attendance, applying them to real data, and accurately predicting future attendance with an root-mean-square error as low as 0.16. Our final contribution is to develop an optimal allocation of classes to rooms based on predicting attendance rather than enrollment, resulting in over 10% savings in room costs with very low risk of room overflows.