Air pollution is responsible for 7 million deaths per year according to World Health Organization (WHO). Thus, it is crucial to dedicate resources to learn and monitor air quality in cities to assist authorities in urban planning as well as bring awareness to people about the impact of air pollution to their everyday life. In our research, we provide the framework and the algorithms, utilising the power of Machine Learning to effectively monitor an environment over time.
In particular, our proposal relies on the willingness of people to participate in environmental air quality campaigns. People can use mobile air quality devices to take readings in their city or their neighbourhood. However, the major issue is when and where these readings should be taken to efficiently monitor the city. People cannot provide an unlimited number of measurements and thus readings should be taken in a way such that information about the environment is maximised. In other words, we need to solve an optimisation problem constrained on the number of readings people can provide over a period of time to facilitate an efficient environment exploration.
In order to solve the problem, we need to model the environment in a certain way as well as a way to measure the information entailed in each reading (since we are interested in gaining the most information by taking a limited number of readings). To do that, we overlay a spatio-temporal stochastic process over the area of interest (Gaussian Processes). Gaussian processes can be used to interpolate over the environment, i.e., predict the air quality value at unobserved locations as well as predict the state of the environment into the future. Importantly, Gaussian Processes can also be used to provide a measure of uncertainty/information about each location in space and time (by utilising predictive variance).
The problem is evolved into taking a set of measurements such that a utility function, created based on predictive variance provided by Gaussian Processes, is maximised. Going a step forward, to solve this problem, we use techniques and algorithms from the broad areas of Artificial Intelligence and Multi-agent systems.
In particular, an intelligent agent can decide when and where measurements should be taken to maximise information gained about the air quality, while at the same time minimise the number of readings needed. The agent can employ greedy search techniques combined with meta-heuristics such as stochastic local search, unsupervised learning (clustering) and random simulations.
The main idea is to simulate the environment over time, asking what if kind of questions. What if i take a measurement now, and one in the night. What if i take measurement downtown or near the home. These kind of questions are answered by running simulations on a cluster computing facility.
Finally, our findings indicate a significant improvement over other approaches.