Optimizing Occupancy Sensor Placement in Smart Environments
This work addresses the need for efficient sensor placement in commercial buildings to enhance energy management, though it appears incremental as it builds on existing sensor networks with a new optimization approach.
The paper tackled the problem of optimizing occupancy sensor placement in smart office environments to improve zone counting accuracy for energy savings, proposing an automatic method that uses integer linear programming and simulations to determine optimal layouts and predict accuracy.
Understanding the locations of occupants in a commercial built environment is critical for realizing energy savings by delivering lighting, heating, and cooling only where it is needed. The key to achieving this goal is being able to recognize zone occupancy in real time, without impeding occupants' activities or compromising privacy. While low-resolution, privacy-preserving time-of-flight (ToF) sensor networks have demonstrated good performance in zone counting, the performance depends on careful sensor placement. To address this issue, we propose an automatic sensor placement method that determines optimal sensor layouts for a given number of sensors, and can predict the counting accuracy of such a layout. In particular, given the geometric constraints of an office environment, we simulate a large number of occupant trajectories. We then formulate the sensor placement problem as an integer linear programming (ILP) problem and solve it with the branch and bound method. We demonstrate the effectiveness of the proposed method based on simulations of several different office environments.