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SALT: Source-Agnostic Localization Technique Based on Context Data from Binary Sensor Networks

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Book cover Ambient Intelligence (AmI 2014)

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Abstract

Localization is a key component for many AAL systems, since the user position can be used for detecting user’s activities and activating devices. While for outdoor scenarios Global Positioning System (GPS) constitutes a reliable and easily available technology, in indoor scenarios, in particular in real homes, GPS is largely unavailable. For this reason, several systems have been proposed for indoor localization. Recently, several algorithms fuse information coming from different sources in order to improve the overall accuracy in monitoring user activities. In this paper we propose a Source-Agnostic Localization Technique, called SALT, that fuses the information (coordinates) provided by a localization system with the information coming from the binary sensor network deployed within the environment. In order to evaluate the proposed framework, we tested our solution by using a previous developed heterogeneous localization systems presented at the international competition EvAAL 2013.

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Acknowledgments

This work was supported by the EU Commission in the framework of the GiraffPlus FP7 project (Contract no. 288173).

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Authors and Affiliations

  1. Information Science and Technologies Institute, National Research Council of Italy, Pisa, Italy

    Filippo Palumbo & Paolo Barsocchi

  2. Department of Computer Science, University of Pisa, Pisa, Italy

    Filippo Palumbo

Authors
  1. Filippo Palumbo
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  2. Paolo Barsocchi
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Corresponding author

Correspondence to Filippo Palumbo .

Editor information

Editors and Affiliations

  1. Technische Universiteit Eindhoven, Eindhoven, The Netherlands

    Emile Aarts

  2. Philips Research Europe, Eindhoven, The Netherlands

    Boris de Ruyter

  3. Technische Universiteit Eindhoven, Eindhoven, The Netherlands

    Panos Markopoulos

  4. Philips Research, Eindhoven, The Netherlands

    Evert van Loenen

  5. Fraunhofer IGD, Darmstadt, Germany

    Reiner Wichert

  6. Technische Universiteit Eindhoven, Eindhoven, The Netherlands

    Ben Schouten

  7. Technische Universiteit Eindhoven, Eindhoven, The Netherlands

    Jacques Terken

  8. Waag Society, Amsterdam, The Netherlands

    Rob Van Kranenburg

  9. Technische Universiteit Eindhoven, Eindhoven, The Netherlands

    Elke Den Ouden

  10. University College Dublin, Dublin 4, Ireland

    Gregory O'Hare

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Palumbo, F., Barsocchi, P. (2014). SALT: Source-Agnostic Localization Technique Based on Context Data from Binary Sensor Networks. In: Aarts, E., et al. Ambient Intelligence. AmI 2014. Lecture Notes in Computer Science(), vol 8850. Springer, Cham. https://doi.org/10.1007/978-3-319-14112-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-14112-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14111-4

  • Online ISBN: 978-3-319-14112-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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