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Schilling, Oliver

Nom
Schilling, Oliver
Affiliation principale
Identifiants
https://libra.unine.ch/handle/123456789/807

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  • Publication
    Accès libre
    Wireless Mesh Networks and Cloud Computing for Real Time Environmental Simulations
    (: Springer, 2014-4-22)
    Kropf, Peter 
    ;
    Schiller, Eryk 
    ;
    Brunner, Philip 
    ;
    Schilling, Oliver 
    ;
    Hunkeler, Daniel 
    ;
    Lapin, Andrei 
    Predicting the influence of drinking water pumping on stream and groundwater levels is essential for sustainable water management. Given the highly dynamic nature of such systems any quantitative analysis must be based on robust and reliable modeling and simulation approaches. The paper presents a wireless mesh-network framework for environmental real time monitoring integrated with a cloud computing environment to execute the hydrogeological simulation model. The simulation results can then be used to sustainably control the pumping stations. The use case of the Emmental catchment and pumping location illustrates the feasibility and effectiveness of our approach even in harsh environmental conditions.
  • Publication
    Accès libre
    Real-time Environmental Monitoring for Cloud-based Hydrogeological Modeling with HydroGeoSphere
    (: IEEE Computer Society, 2014)
    Lapin, Andrei 
    ;
    Schiller, Eryk 
    ;
    Kropf, Peter 
    ;
    Schilling, Oliver 
    ;
    Brunner, Philip 
    ;
    Jamakovic-Kapic, A.
    ;
    Braun, T.
    ;
    Maffioletti, S.
    This paper describes an architecture for real-time environmental modeling. It consists of a wireless mesh network equipped with sensors and a cloud-based infrastructure to perform real-time environmental sim- ulations using a physics-based model combined with an Ensemble Kalman Filter. The purpose of the system is to optimize groundwater abstraction close to a river. These initial studies demonstrate that the cloud infrastructure can simultaneously compute a large number of simula- tions, thus allowing for the implementation of Ensemble Kalman Filters in real-time.