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Mediterranean Conference on Pattern Recognition and Artificial Intelligence

MedPRAI 2019: Pattern Recognition and Artificial Intelligence pp 1–8Cite as

Room-Level Indoor Localization with Artificial Neural Networks

Part of the Communications in Computer and Information Science book series (CCIS,volume 1144)

Abstract

Indoor localization system determines the location of the users or some assets in indoor environments. There are important applications of indoor localization including smart home systems, indoor navigation and tracking systems. In this work, a reliable neural network model is developed for localizing users in room level. Model is based on Wi-Fi signals received by the users’ devices at different rooms from various Wi-Fi access points. A neural network with two hidden layers with sigmoid activation functions is trained with back-propagation optimizing collected signal data. Some of the signals are set to 0 during the training process, which gives significant stability to the model under the conditions where some of the data required for prediction are not available. An additional dataset is collected for the evaluation in addition to the existing datasets. Performance of the model on the existing datasets as well as the new collected dataset is discussed and evaluated. Results are promising in terms of reliability and accuracy.

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Authors and Affiliations

  1. Hacettepe University, Ankara, Turkey

    Ahmet Serdar Karadeniz & Mehmet Önder Efe

Authors
  1. Ahmet Serdar Karadeniz
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  2. Mehmet Önder Efe
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Corresponding author

Correspondence to Ahmet Serdar Karadeniz .

Editor information

Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Chawki Djeddi

  2. Istanbul Sabahattin Zaim University, Istanbul, Turkey

    Akhtar Jamil

  3. Bahria University, Islamabad, Pakistan

    Prof. Imran Siddiqi

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Serdar Karadeniz, A., Efe, M.Ö. (2020). Room-Level Indoor Localization with Artificial Neural Networks. In: Djeddi, C., Jamil, A., Siddiqi, I. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2019. Communications in Computer and Information Science, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-030-37548-5_1

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  • DOI: https://doi.org/10.1007/978-3-030-37548-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37547-8

  • Online ISBN: 978-3-030-37548-5

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