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AWESOM: Automatic Discrete Partitioning of Indoor Spaces for WiFi Fingerprinting

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Pervasive Computing (Pervasive 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7319))

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Abstract

WiFi fingerprinting is currently one of the most popular techniques for indoor localization as it provides reasonable positioning accuracy while at the same time being able to exploit existing wireless infrastructure. To facilitate calibration efforts and to overcome fluctuations in location measurements, many indoor WiFi positioning systems utilize a discrete partitioning, e.g., a grid or a topological map, of the space where the positioning is being deployed. A major limitation of this approach, however, is that instead of considering spatial similarities in the signal environment, the partitioning is typically based on an uniform division of the space or topological constraints (e.g., rooms and walls). This can significantly decrease positioning accuracy when the signal environment is not sufficiently stable across all partitions. Moreover, current solutions provide no support for identifying partitions that are not compatible with the current wireless deployment. To overcome these limitations, we propose AWESOM (Activations Weighted by the Euclidean-distance using Self-Organizing Maps), a novel measure for automatically creating a discrete partitioning of the space where the WiFi positioning is being deployed. In addition to enabling automatic construction of a discrete partitioning, AWESOM provides a measure for evaluating the goodness of a given partitioning for a particular access point deployment. AWESOM also enables identifying partitions where additional access points should be deployed. We demonstrate the usefulness of AWESOM using data collected from two large scale deployments of a proprietary wireless positioning system in a hypermarket environment.

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Author information

Authors and Affiliations

  1. Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, P.O. Box 68, FI-00014, Finland

    Teemu Pulkkinen & Petteri Nurmi

Authors
  1. Teemu Pulkkinen
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  2. Petteri Nurmi
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Editor information

Editors and Affiliations

  1. School of Information Technologies, University of Sydney, Australia

    Judy Kay

  2. DFKI GmbH, Embedded Intelligence, Trippstadter Straße 122, 67663, Kaiserslautern, Germany

    Paul Lukowicz

  3. Faculty of Environment and Information Studies, Keio University, 5322, Endo, 252-8520, Fujisawa City, Kanagawa, Japan

    Hideyuki Tokuda

  4. School of Computing Science, Newcastle University, UK

    Patrick Olivier

  5. DFKI GmbH, Stuhlsatzenhausweg 3, 66123, Saarbrücken, Germany

    Antonio Krüger

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© 2012 Springer-Verlag Berlin Heidelberg

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Pulkkinen, T., Nurmi, P. (2012). AWESOM: Automatic Discrete Partitioning of Indoor Spaces for WiFi Fingerprinting. In: Kay, J., Lukowicz, P., Tokuda, H., Olivier, P., Krüger, A. (eds) Pervasive Computing. Pervasive 2012. Lecture Notes in Computer Science, vol 7319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31205-2_17

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  • DOI: https://doi.org/10.1007/978-3-642-31205-2_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31204-5

  • Online ISBN: 978-3-642-31205-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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