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Resilient Environmental Monitoring Utilizing a Machine Learning Approach

  • Dan HäberleinEmail author
  • Lars Kafurke
  • Sebastian Höfer
  • Bogdan Franczyk
  • Bernhard Jung
  • Erik Berger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11508)

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.

Keywords

Environmental monitoring Virtual sensor Machine learning Volunteered geographic information 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dan Häberlein
    • 1
    Email author
  • Lars Kafurke
    • 1
  • Sebastian Höfer
    • 1
  • Bogdan Franczyk
    • 1
  • Bernhard Jung
    • 2
  • Erik Berger
    • 2
  1. 1.Institute of Business Information SystemsUniversity of LeipzigLeipzigGermany
  2. 2.Institute of Computer ScienceTechnical University Bergakademie FreibergFreibergGermany

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