Estimating Room Occupancy in a Smart Campus using WiFi Soft Sensors

Abstract

Universities worldwide are experiencing a surge in enrolments, therefore campus estate managers are seeking continuous data on attendance patterns so as to optimize the usage of classroom space. While prior works have measured room occupancy via hardware sensor instrumentation, in this paper we explore the use of pervasive WiFi infrastructure for estimating attendance. In a dense campus environment, WiFi connectivity counts are poor estimators of room occupancy since they are polluted by adjoining rooms, outdoor walkways, and network load balancing. The main contribution of this work is to develop new ways to distinguish and filter out WiFi-connected users outside of the lecture room of interest, and feed such data to a regression analyser to estimate room occupancy. We evaluate our technique across lecture theatres of varying size in our campus, and show that their accuracy approaches that of hardware sensors without incurring cost and effort of installing and maintaining them.