# Optimizing Occupancy Sensor Placement in Smart Environments

arXiv: https://arxiv.org/abs/2602.21098 · HTML: https://mlsanity.com/paper/2602.21098/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 24 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting](https://mlsanity.com/paper/2012.01526/) (2020)
- [MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction](https://mlsanity.com/paper/2201.07189/) (2022)
- [Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning](https://mlsanity.com/paper/2008.04047/) (2020)
- [Low-Resolution Overhead Thermal Tripwire for Occupancy Estimation](https://mlsanity.com/paper/2004.05685/) (2020)

## Abstract (arXiv)

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.