Bayesian Optimisation for Active Monitoring of Air Pollution

Abstract

Air pollution is one of the leading causes of mortality globally, resulting in millions of deaths each year. Efficient monitoring is important to measure exposure and enforce legal limits. New low-cost sensors can be deployed in greater numbers and in more varied locations, motivating the problem of efficient automated placement. Previous work suggests Bayesian optimisation is an appropriate method, but only considered a satellite data set, with data aggregated over all altitudes. It is ground-level pollution, that humans breathe, which matters most. We improve on those results using hierarchical models and evaluate our models on urban pollution data in London to show that Bayesian optimisation can be successfully applied to the problem.

Grey-Box Bayesian Optimization for Sensor Placement in Assisted Living Environments

“…A future work direction is to apply transfer learning to the expected information gain and use this information in other environments. Another future direction is to assess the effectiveness of DGBO in other emerging applications such as air pollution monitoring (Hellan, Lucas, and Goddard 2022), wildfire monitoring (Gholami et al 2021), and effective emergency response (Ghosh and Varakantham 2018). Finally, we have explored the optimal placement of a sensor that is non-intrusive, inexpensive, and commonly used for activity detection in aging-in-place settings.…”

“…When this model represents f (x) accurately, the optimizer can perform more effective exploration/exploitation. A wide range of problems have been recently solved using BO, from optimization over permutation spaces (Deshwal et al 2022) and combinatorial spaces (Deshwal et al 2020(Deshwal et al , 2023 to setting up sensor networks for air quality monitoring (Hellan, Lucas, and Goddard 2022). However, the application of BO to optimize the sensor placement for indoor activity recognition has not been explored in previous work.…”