Resilient Environmental Monitoring Utilizing a Machine Learning Approach

摘要

A wide range of regulations is established to protect citizens health from the noxious consequences of aerosols, e.g. particulate matter (PM10). To ensure a public information and the compliance to given regulations, a resilient environmental sensor network is necessary. This paper presents a machine learning approach which utilizes low-cost platforms to build a resilient sensor network. In particular, malfunctions are compensated by learning virtual models of various particulate matter sensors. Such virtualized sensors are already utilized in the field of pro-prioceptive robotics and are comparable to a digital twins definition. Several experiments show the proposed method yields PM10 estimates and forecasts similar to high-performance sensors.