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Mobility Prediction in Wireless Networks Using Deep Learning Algorithm

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Advances of Science and Technology (ICAST 2019)

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Abstract

Recently, wireless-technologies and their users are rising due to productions of sensor-networks, mobile devices, and supporting applications. Location Based Services (LBS) such as mobility prediction is a key technology for the success of IoT. However, mobility prediction in wireless network is too challenging since the network becomes very condensed and it changes dynamically. In this paper, we propose a deep neural network based mobility prediction in wireless environment to provide an adaptive and accurate positioning system to mobile users. In the system development processes, firstly, we collect RSS values from three Unmanned Aerial Vehicle Base Stations (UAV-BSs). Secondly, we preprocess the collected data to get refine features and to avoid null records or cells. Thirdly, we exhaustively train the Long-short term memory (LSTM) network to find the optimum model for mobility prediction of the smartphone users. Finally, we test the designed model to evaluate system performances. The performance of the proposed system also compared with Multilayer Perceptron (MLP) algorithm to assess the soundness of mobility prediction model. The LSTM outperforms the MLP algorithm in different evaluating parameters.

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Acknowledgments

This work was partially supported by Ministry of Science and Technology (MOST) under Grant numbers 108-2634-F-009-006 and 107-2221-E-027-025.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei, Taiwan

    Abebe Belay Adege, Getaneh Berie Tarekegn & Yirga Yayeh

  2. Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan

    Hsin-Piao Lin

Authors
  1. Abebe Belay Adege
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  2. Hsin-Piao Lin
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  3. Getaneh Berie Tarekegn
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  4. Yirga Yayeh
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Corresponding author

Correspondence to Abebe Belay Adege .

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Editors and Affiliations

  1. Bahir Dar University, Bahir Dar, Ethiopia

    Nigus Gabbiye Habtu

  2. Bahir Dar University, Bahir Dar, Ethiopia

    Delele Worku Ayele

  3. Bahir Dar University, Bahir Dar, Ethiopia

    Solomon Workneh Fanta

  4. Bahir Dar University, Bahir Dar, Ethiopia

    Bimrew Tamrat Admasu

  5. Bahir Dar University, Bahir Dar, Ethiopia

    Mekuanint Agegnehu Bitew

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Adege, A.B., Lin, HP., Tarekegn, G.B., Yayeh, Y. (2020). Mobility Prediction in Wireless Networks Using Deep Learning Algorithm. In: Habtu, N., Ayele, D., Fanta, S., Admasu, B., Bitew, M. (eds) Advances of Science and Technology. ICAST 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 308. Springer, Cham. https://doi.org/10.1007/978-3-030-43690-2_31

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  • DOI: https://doi.org/10.1007/978-3-030-43690-2_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43689-6

  • Online ISBN: 978-3-030-43690-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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