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Indoor Positioning System for Ubiquitous Computing Environments

  • Conference paper
  • First Online:
Intelligent Data Engineering and Automated Learning – IDEAL 2021 (IDEAL 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13113))

  • 1653 Accesses

Abstract

We developed an Indoor Positioning System (IPS) as part of the effort of creating Ubicomp applications with user interfaces distributed across different co-located devices. It relies on a Client that runs on the devices that we intend to locate and a Server that determines their positions. It currently supports three positioning methods: fingerprinting, trilateration and proximity. Bluetooth Low Energy and Wi-Fi are used as the underlying technologies for the positioning methods. We tested multiple machine learning algorithms during the development of the system to choose the ones providing satisfactory results. A Mean Absolute Error around or below 1 m and 95th percentile errors in the 2 m range were considered acceptable according to the type of target applications. We were also able to integrate the system into our framework and built a cross-device application that took advantage of it.

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Notes

  1. 1.

    https://github.com/kamalshadi/Localization.

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Author information

Authors and Affiliations

  1. NOVA LINCS, NOVA University of Lisbon, Lisbon, Portugal

    Pedro Albuquerque Santos, Rui Porfírio, Rui Neves Madeira & Nuno Correia

  2. Sustain.RD, ESTSetúbal, Polytechnique Institute of Setúbal, Setúbal, Portugal

    Pedro Albuquerque Santos & Rui Neves Madeira

Authors
  1. Pedro Albuquerque Santos
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  2. Rui Porfírio
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  3. Rui Neves Madeira
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  4. Nuno Correia
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Corresponding authors

Correspondence to Pedro Albuquerque Santos , Rui Porfírio , Rui Neves Madeira or Nuno Correia .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Universidad Politecnica de Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Manchester, Manchester, UK

    Richard Allmendinger

  5. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  6. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  7. Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  8. University of Minho, Braga, Portugal

    Paulo Novais

  9. NOVA University of Lisbon, Lisbon, Portugal

    Susana Nascimento

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Santos, P.A., Porfírio, R., Madeira, R.N., Correia, N. (2021). Indoor Positioning System for Ubiquitous Computing Environments. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2021. IDEAL 2021. Lecture Notes in Computer Science(), vol 13113. Springer, Cham. https://doi.org/10.1007/978-3-030-91608-4_59

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  • DOI: https://doi.org/10.1007/978-3-030-91608-4_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91607-7

  • Online ISBN: 978-3-030-91608-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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