Approximate Computational Approaches for Bayesian Sensor Placement in High Dimensions
This work addresses sensor placement for inference in high-dimensional applications like environmental monitoring, but it is incremental as it builds on existing mutual information and Bayesian methods.
The paper tackles the problem of efficiently selecting sensor locations to infer quantities of interest in high-dimensional settings by proposing an approach that computes an approximate lower bound of mutual information in lower dimensions and uses Bayesian optimization to reduce evaluations. The result demonstrates effectiveness and efficiency in a simulated chemical release accident scenario.
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.