Unsupervised and Hybrid Approaches for On-line RFID Localization with Mixed Context Knowledge

  • Christoph Scholz
  • Martin Atzmueller
  • Gerd Stumme
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)


Indoor localization of humans is still a complex problem, especially in resource-constrained environments, e. g., if there is only a small number of data available over time. We address this problem using active RFID technology and focus on room-level localization. We propose several unsupervised localization approaches and compare their accuracy to state-of-the art unsupervised and supervised localization methods. In addition, we combine unsupervised and supervised methods into a hybrid approach using different types of mixed context knowledge. We show, that the new unsupervised approaches significantly outperform state-of-the-art supervised methods, and that the hybrid approach performs best in our application setting. We analyze real world data collected at a two days evaluation of our working group management system MyGroup.


Signal Strength Receive Signal Strength Localization Accuracy Unsupervised Approach Supervise Approach 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Christoph Scholz
    • 1
  • Martin Atzmueller
    • 1
  • Gerd Stumme
    • 1
  1. 1.Knowledge & Data Engineering GroupUniversity of KasselGermany

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