Using Personnel Movements for Indoor Autonomous Environment Discovery
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
PreHeat: controlling home heating using occupancy prediction
Proceedings of the 13th international conference on Ubiquitous computing
Smart blueprints: automatically generated maps of homes and the devices within them
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Matchstick: a room-to-room thermal model for predicting indoor temperature from wireless sensor data
Proceedings of the 12th international conference on Information processing in sensor networks
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Sensor and actuator networks are often installed in buildings for energy-related applications such as lighting and climate control. Such systems require metadata about the deployed hardware (e.g. which room each is in, what the function of each room is) in order to operate effectively. In this paper we present methods to automatically determine such metadata, in particular the room connectivity graph (i.e., which rooms share a doorway/interior window). Crucially, our method works with just one sensor unit per room, does not require special placement of any of the sensors, and can therefore work on data from existing widely-deployed applications (such as burglar alarms). We apply this method to a 30-day data set from single per-room sensor units deployed in two residential homes in the United Kingdom. Room connectivity is determined based on: spillover of artificial light between rooms; occupancy detections due to movement between rooms; and a fusion of the two. The fusion of both techniques is shown to work better than either technique alone, with a 93% true positive rate and 0.5% false positive rate (aggregate across both houses), and a convergence time of under a week.