自动优化智能空间中传感器布局，提升人数识别精度。
Optimizing Occupancy Sensor Placement in Smart Environments
- 基于几何约束模拟人员轨迹，构建传感器布局的整数线性规划模型。
- 在多个办公环境仿真中，所提方法可预测布局的计数准确率。
- 适合需隐私保护且精准感知人员分布的智能建筑节能场景。
在商业建筑环境中，了解人员位置对于仅在需要时提供照明、供暖和制冷以实现节能至关重要。实现这一目标的关键在于实时识别区域占用情况，同时不干扰人员活动或侵犯隐私。尽管低分辨率、隐私保护的飞行时间（ToF）传感器网络在区域计数方面已表现出良好性能，但其效果依赖于传感器的精心布置。为此，本文提出一种自动传感器布置方法，可针对给定数量的传感器确定最优布局，并预测该布局的计数准确性。具体而言，基于办公室环境的几何约束，我们模拟了大量人员移动轨迹，并将传感器布置问题建模为整数线性规划（ILP）问题，采用分支定界法求解。通过在多个不同办公环境中的仿真，验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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