Designing an Optimal Sensor Network via Minimizing Information Loss

2025-12-05

AI Summary

This paper addresses information loss in sensor placement for spatiotemporal dynamic process monitoring. We propose an optimal sensor network design method that integrates physics-based modeling with Bayesian experimental design. Our core contribution is a separable Gaussian–Markov prior explicitly incorporating the temporal dimension, coupled with sparse variational inference and large-scale physics simulations to define a novel, model-driven sensor placement criterion within a Bayesian framework. The method significantly enhances information acquisition efficiency and predictive accuracy under strict sensor budget constraints. In an empirical study on urban air temperature monitoring in Phoenix, our approach achieves superior data representativeness and lower prediction error using fewer sensors than both random and quasi-random baselines—demonstrating its effectiveness and practical utility in real-world applications.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

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.

Problem

• Designing optimal sensor placement for spatiotemporal monitoring
• Integrating physics-based simulations with Bayesian experimental design
• Minimizing information loss from simulated data efficiently

Innovation

• Integrating physics simulations with Bayesian design
• Using sparse variational inference for optimization
• Applying Gauss-Markov priors for sensor placement