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Mobility Prediction in Wireless Networks Using Deep Learning Algorithm

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

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.

Keywords

Long-short term memory Location based services Mobility prediction 

Notes

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.

References

  1. 1.
    Pathirana, P.N., Savkin, A.V., Jha, S.: Location estimation and trajectory prediction for cellular networks with mobile base stations. IEEE Trans. Veh. Technol. 53(6), 1903–1913 (2004)CrossRefGoogle Scholar
  2. 2.
    Versichele, M., Neutens, T., Delafontaine, M., Van de Weghe, N.: The use of Bluetooth for analyzing spatiotemporal dynamics of human movement at mass events. Appl. Geogr. 32(2), 208–220 (2012)CrossRefGoogle Scholar
  3. 3.
    Pan, J.J., Pan, S.J., Yin, J., Ni, L.M., Yang, Q.: Tracking mobile users in wireless networks via semi-supervised colocalization. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 587–600 (2012)CrossRefGoogle Scholar
  4. 4.
    Anisetti, M., Bellandi, V., Damiani, E., Reale, S.: Advanced localization of mobile terminal. In: ISCIT 2007 - 2007 International Symposium on Communications and Information Technologies, pp. 1071–1076, February 2007Google Scholar
  5. 5.
    Laoudias, C., Moreira, A., Kim, S., Lee, S., Wirola, L., Fischione, C.: A survey of enabling technologies for network localization, tracking, and navigation. IEEE Commun. Surv. Tutor. 20, 3607–3644 (2018)CrossRefGoogle Scholar
  6. 6.
    Adege, A.B., et al.: Applying deep neural network (DNN) for large-scale indoor localization using feed-forward neural network (FFNN) algorithm. In: Proceedings of the 4th IEEE International Conference on Applied System Invention, ICASI 2018, vol. 11, pp. 814–817 (2018)Google Scholar
  7. 7.
    Yuanfeng, D., Dongkai, Y., Huilin, Y., Chundi, X.: Flexible indoor localization and tracking system based on mobile phone. J. Netw. Comput. Appl. 69, 107–116 (2016)CrossRefGoogle Scholar
  8. 8.
    Zanella, A., et al.: Internet of things for smart cities. IEEE Internet Things J. 1(1), 22–32 (2017)CrossRefGoogle Scholar
  9. 9.
    Anagnostopoulos, T., Anagnostopoulos, C., Hadjiefthymiades, S.: An adaptive machine learning algorithm for location prediction. Int. J. Wirel. Inf. Netw. 18(2), 88–99 (2011)CrossRefGoogle Scholar
  10. 10.
    Oguejiofor, O.S., Okorogu, V.N., Abe, A., Osuesu, B.O.: Outdoor localization system using RSSI measurement of wireless sensor network outdoor localization system using RSSI measurement of wireless sensor network. Int. J. Innov. Technol. Explor. Eng. 2(2), 1–7 (2015)Google Scholar
  11. 11.
    Sri, M.S.: Tracking and Positioning of Mobile in telecommunication 1, vol. 2, no. 1, pp. 1–47 (2015)Google Scholar
  12. 12.
    Samiei, M., Mehrjoo, M., Pirzade, B.: Advances of positioning methods in cellular networks. In: International Conference on Communications Engineering, pp. 174–178 (2010)Google Scholar
  13. 13.
    Lu, M., Liu, S., Liu, P.: The research of real-time UAV inspection system for photovoltaic power station based on 4G private network. J. Comput. 28(2), 189–196 (2017)MathSciNetGoogle Scholar

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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