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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2019: Artificial Intelligence and Soft Computing pp 3–13Cite as

Indoor Localization Using Cluster Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11509))

Abstract

One of the key requirements of context based systems and intelligent environments is a user’s location. Numerous indoor localization solutions have been proposed. In this paper, we propose an enhancement to an already implemented indoor localization algorithm that utilizes the JUDOCA operator to linearly find a match to an input image within a geo-tagged dataset of pre-stored images. The proposed approach is based on k-medoids cluster analysis, which is used to compare distances calculated with the same JUDOCA operator used in the original algorithm in an attempt to enhance its execution time. The results showed that the proposed approach introduced an enhancement in the execution speed of around 10 times compared to the original approach.

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Author information

Authors and Affiliations

  1. German University in Cairo, Cairo, Egypt

    Ramy Aboul Naga & Rimon Elias

  2. British University in Egypt, Cairo, Egypt

    Amal El Nahas

Authors
  1. Ramy Aboul Naga
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  2. Rimon Elias
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  3. Amal El Nahas
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Corresponding author

Correspondence to Ramy Aboul Naga .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Aboul Naga, R., Elias, R., El Nahas, A. (2019). Indoor Localization Using Cluster Analysis. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_1

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  • DOI: https://doi.org/10.1007/978-3-030-20915-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20914-8

  • Online ISBN: 978-3-030-20915-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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