Sensor Data Visualisation: A Composition-Based Approach to Support Domain Variability

  • Ivan Logre
  • Sébastien Mosser
  • Philippe Collet
  • Michel Riveill
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8569)


In the context of the Internet of Things, sensors are surrounding our environment. These small pieces of electronics are inserted in everyday life’s elements (e.g., cars, doors, radiators, smartphones) and continuously collect information about their environment. One of the biggest challenges is to support the development of accurate monitoring dashboard to visualise such data. The one-size-fits-all paradigm does not apply in this context, as user’s roles are variable and impact the way data should be visualised: a building manager does not need to work on the same data as classical users. This paper presents an approach based on model composition techniques to support the development of such monitoring dashboards, taking into account the domain variability. This variability is supported at both implementation and modelling levels. The results are validated on a case study named SmartCampus, involving sensors deployed in a real academic campus.


Variability Data visualisation Sensors Model composition 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ivan Logre
    • 1
  • Sébastien Mosser
    • 1
  • Philippe Collet
    • 1
  • Michel Riveill
    • 1
  1. 1.CNRS, I3S, UMR 7271Université Nice – Sophia AntipolisSophia AntipolisFrance

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