Abstract
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 proprioceptive robotics [1] and are comparable to a digital twins definition. Several experiments show the proposed method yields PM10 estimates and forecasts similar to high-performance sensors.
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Häberlein, D., Kafurke, L., Höfer, S., Franczyk, B., Jung, B., Berger, E. (2019). Resilient Environmental Monitoring Utilizing a Machine Learning Approach. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_8
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