Optimizing Occupancy Sensor Placement in Smart Environments

TL;DR

This paper presents an automated method for optimally placing occupancy sensors in smart environments to improve zone counting accuracy, using simulations and integer linear programming.

Contribution

It introduces a novel ILP-based approach for automatic sensor placement tailored to specific environments, enhancing occupancy detection performance.

Findings

Sensor placement significantly impacts counting accuracy.

The ILP method outperforms heuristic placement strategies.

Simulations confirm improved occupancy detection in various office layouts.

Abstract

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.