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Optimizing Occupancy Sensor Placement in Smart Environments

Hao Lu, Richard J. Radke

TL;DR

This work proposes 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, and demonstrates the effectiveness of the proposed method based on simulations of several different office environments.

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.

Optimizing Occupancy Sensor Placement in Smart Environments

TL;DR

This work proposes 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, and demonstrates the effectiveness of the proposed method based on simulations of several different office environments.

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.
Paper Structure (16 sections, 2 equations, 16 figures, 1 table)

This paper contains 16 sections, 2 equations, 16 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Occupant Trajectory Modelling
  4. Floor Plan Labeling
  5. Generating Occupant Trajectories
  6. Sensor System Modeling
  7. Sensor Placement Optimization
  8. Experimental Results
  9. Test Environments
  10. Unity 3D Simulation
  11. Evaluation Metric
  12. Results
  13. Role of the Dilation Parameter
  14. Choosing the Number of Sensors
  15. Effects of Differing Trajectory Models
  16. ...and 1 more sections

Figures (16)

  • Figure 1: (a) An example floor plan of an office suite. (b) The color-labeled floor plan of the office suite. There are 6 labels: walls (black), obstacles (grey), doorways (brown), areas of interest (red), zone boundaries (green), and walkable regions (white).
  • Figure 2: Shortest paths between the same two endpoints in cases that without random obstacles (a) and with random obstacles (b).
  • Figure 3: Shortest paths between the same two endpoints without the doorway penalty (blue) and with the doorway penalty (yellow).
  • Figure 4: Shortest paths between the same two endpoints without the wall penalty (blue) and with the wall penalty (yellow).
  • Figure 5: A heatmap of 3000 randomly generated occupant trajectories in an office suite.
  • ...and 11 more figures