Approximate Computational Approaches for Bayesian Sensor Placement in High Dimensions

TL;DR

This paper introduces an approximate method to optimize sensor placement in high-dimensional spaces by estimating a lower bound of mutual information, using Bayesian optimization to efficiently identify optimal locations for inferring quantities of interest.

Contribution

It proposes a novel approach to approximate mutual information in high dimensions and integrates Bayesian optimization for efficient sensor placement.

Findings

Effective in locating sources in simulated chemical release

Reduces computational complexity of mutual information estimation

Achieves accurate QoI inference with fewer sensors

Abstract

Since the cost of installing and maintaining sensors is usually high, sensor locations are always strategically selected. For those aiming at inferring certain quantities of interest (QoI), it is desirable to explore the dependency between sensor measurements and QoI. One of the most popular metric for the dependency is mutual information which naturally measures how much information about one variable can be obtained given the other. However, computing mutual information is always challenging, and the result is unreliable in high dimension. In this paper, we propose an approach to find an approximate lower bound of mutual information and compute it in a lower dimension. Then, sensors are placed where highest mutual information (lower bound) is achieved and QoI is inferred via Bayes rule given sensor measurements. In addition, Bayesian optimization is introduced to provide a continuous mutual information surface over the domain and thus reduce the number of evaluations. A chemical release accident is simulated where multiple sensors are placed to locate the source of the release. The result shows that the proposed approach is both effective and efficient in inferring QoI.