用物理仿真与贝叶斯方法优化传感器布局，减少信息损失。
Designing an Optimal Sensor Network via Minimizing Information Loss
- 结合物理模拟与贝叶斯设计，以最小化信息损失为目标优化传感器位置。
- 在凤凰城气温监测中，少量传感器下性能优于随机布设。
- 适用于有限传感器条件下的高精度环境监测系统设计。
最优实验设计是统计学经典课题，广泛应用于传感器部署以监测时空过程。本文提出一种新型模型驱动的传感器布置准则，融合物理基仿真与贝叶斯实验设计思想，通过稀疏变分推断和可分离高斯-马尔可夫先验，实现高效优化，旨在最小化从仿真数据中丢失的信息。我们基于最先进的物理基模拟，在亚利桑那州凤凰城的气温监测案例中验证该方法，结果表明，当传感器数量有限时，本框架显著优于随机或准随机采样。研究还讨论了复杂建模工具与实际部署的可行性，为真实世界应用提供支持。
原文摘要 · Abstract (English)
Optimal experimental design is a classic topic in statistics, with many well-studied problems, applications, and solutions. The design problem we study is the placement of sensors to monitor spatiotemporal processes, explicitly accounting for the temporal dimension in our modeling and optimization. We observe that recent advancements in computational sciences often yield large datasets based on physics-based simulations, which are rarely leveraged in experimental design. We introduce a novel model-based sensor placement criterion, along with a highly-efficient optimization algorithm, which integrates physics-based simulations and Bayesian experimental design principles to identify sensor networks that "minimize information loss" from simulated data. Our technique relies on sparse variational inference and (separable) Gauss-Markov priors, and thus may adapt many techniques from Bayesian experimental design. We validate our method through a case study monitoring air temperature in Phoenix, Arizona, using state-of-the-art physics-based simulations. Our results show our framework to be superior to random or quasi-random sampling, particularly with a limited number of sensors. We conclude by discussing practical considerations and implications of our framework, including more complex modeling tools and real-world deployments.
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