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A Use Case in Semantic Modelling and Ranking for the Sensor Web

  • Liliana Cabral
  • Michael Compton
  • Heiko Müller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8797)

Abstract

Agricultural decision support systems are an important application of real-time sensing and environmental monitoring. With the continuing increase in the number of sensors deployed, selecting sensors that are fit for purpose is a growing challenge. Ontologies that represent sensors and observations can form the basis for semantic sensor data infrastructures. Such ontologies may help to cope with the problems of sensor discovery, data integration, and re-use, but need to be used in conjunction with algorithms for sensor selection and ranking. This paper describes a method for selecting and ranking sensors based on the requirements of predictive models. It discusses a Viticulture use case that demonstrates the complexity of semantic modelling and reasoning for the automated ranking of sensors according to the requirements on environmental variables as input to predictive analytical models. The quality of the ranking is validated against the quality of outputs of a predictive model using different sensors.

Keywords

Semantic sensor data Sensor ranking Sensor Cloud Ontology Viticulture Predictive analytical models 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Liliana Cabral
    • 1
  • Michael Compton
    • 2
  • Heiko Müller
    • 1
  1. 1.Digital Productivity and Services FlagshipCSIROHobartAustralia
  2. 2.Digital Productivity and Services FlagshipCSIROCanberraAustralia

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