Expanding Sensor Networks to Automate Knowledge Acquisition

  • Kenneth Conroy
  • Gregory C. May
  • Mark Roantree
  • Giles Warrington
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7051)


The availability of accurate, low-cost sensors to scientists has resulted in widespread deployment in a variety of sporting and health environments. The sensor data output is often in a raw, proprietary or unstructured format. As a result, it is often difficult to query multiple sensors for complex properties or actions. In our research, we deploy a heterogeneous sensor network to detect the various biological and physiological properties in athletes during training activities. The goal for exercise physiologists is to quickly identify key intervals in exercise such as moments of stress or fatigue. This is not currently possible because of low level sensors and a lack of query language support. Thus, our motivation is to expand the sensor network with a contextual layer that enriches raw sensor data, so that it can be exploited by a high level query language. To achieve this, the domain expert specifies events in a tradiational event-condition-action format to deliver the required contextual enrichment.


Sensor Network Sensor Data Vector Magnitude Context Integration Merge Ontology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kenneth Conroy
    • 1
  • Gregory C. May
    • 1
  • Mark Roantree
    • 2
  • Giles Warrington
    • 1
  1. 1.CLARITY: Centre for Sensor Web TechnologiesDublin City UniversityIreland
  2. 2.Interoperable Systems Group, School of ComputingDublin City UniversityDublinIreland

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