Indoor Occupancy Prediction using an IoT Platform

Current research in indoor sensor networks has pointed out an emerging interest in occupancy detection for Building Information Management (BIM) because buildings use 68% of Canadas energy in operation and contribute 17% of greenhouse gas (GHG) emissions. This research paper aims at developing a non-intrusive sensing method for predicting occupancy towards reducing building emission while also promoting a comfortable and productive working environment, while retaining the privacy of occupants. Towards this end, an IoT platform consisting of three main components: the edge computing environment, cloud based infrastructure, and network communication, together create a robust open source IoT architecture. The open source IoT architecture employs temperature, humidity, and pressure sensors for observing ambient environmental characteristics while combining PIR motion sensors, CO2, and sound detectors. An occupancy detection model is then developed by applying Support Vector Machine (SVM) to predict occupancy patterns from the incoming IoT sensor data. This platform is a low-cost and highly scalable both in terms of the variety of on board sensors and portability of the sensor nodes, which makes it well suited for multiple applications related to occupancy and environmental monitoring.