Mobility Prediction in Wireless Networks Using Deep Learning Algorithm

  • Abebe Belay AdegeEmail author
  • Hsin-Piao Lin
  • Getaneh Berie Tarekegn
  • Yirga Yayeh
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 308)


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.


Long-short term memory Location based services Mobility prediction 



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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Copyright information

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

Authors and Affiliations

  • Abebe Belay Adege
    • 1
    Email author
  • Hsin-Piao Lin
    • 2
  • Getaneh Berie Tarekegn
    • 1
  • Yirga Yayeh
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceNational Taipei University of TechnologyTaipeiTaiwan
  2. 2.Department of Electronic EngineeringNational Taipei University of TechnologyTaipeiTaiwan

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