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Machine Learning Based Localization of LoRa Mobile Wireless Nodes Using a Novel Sectorization Method

Author

Listed:
  • Madiyar Nurgaliyev

    (Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, Kazakhstan)

  • Askhat Bolatbek

    (Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, Kazakhstan)

  • Batyrbek Zholamanov

    (Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, Kazakhstan)

  • Ahmet Saymbetov

    (Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, Kazakhstan)

  • Kymbat Kopbay

    (Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, Kazakhstan)

  • Evan Yershov

    (Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, Kazakhstan)

  • Sayat Orynbassar

    (Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, Kazakhstan)

  • Gulbakhar Dosymbetova

    (Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, Kazakhstan)

  • Ainur Kapparova

    (Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, Kazakhstan)

  • Nurzhigit Kuttybay

    (Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, Kazakhstan)

  • Nursultan Koshkarbay

    (Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi, Almaty 050040, Kazakhstan)

Abstract

Indoor localization of wireless nodes is a relevant task for wireless sensor networks with mobile nodes using mobile robots. Despite the fact that outdoor localization is successfully performed by Global Positioning System (GPS) technology, indoor environments face several challenges due to multipath signal propagation, reflections from walls and objects, along with noise and interference. This results in the need for the development of new localization techniques. In this paper, Long-Range Wide-Area Network (LoRaWAN) technology is employed to address localization problems. A novel approach is proposed, based on the preliminary division of the room into sectors using a Received Signal Strength Indicator (RSSI) fingerprinting technique combined with machine learning (ML). Among various ML methods, the Gated Recurrent Unit (GRU) model reached the most accurate results, achieving localization accuracies of 94.54%, 91.02%, and 85.12% across three scenarios with a division into 256 sectors. Analysis of the cumulative error distribution function revealed the average localization error of 0.384 m, while the mean absolute error reached 0.246 m. These results demonstrate that the proposed sectorization method effectively mitigates the effects of noise and nonlinear signal propagation, ensuring precise localization of mobile nodes indoors.

Suggested Citation

  • Madiyar Nurgaliyev & Askhat Bolatbek & Batyrbek Zholamanov & Ahmet Saymbetov & Kymbat Kopbay & Evan Yershov & Sayat Orynbassar & Gulbakhar Dosymbetova & Ainur Kapparova & Nurzhigit Kuttybay & Nursulta, 2024. "Machine Learning Based Localization of LoRa Mobile Wireless Nodes Using a Novel Sectorization Method," Future Internet, MDPI, vol. 16(12), pages 1-28, December.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:12:p:450-:d:1535192
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    References listed on IDEAS

    as
    1. Micael Coutinho & Jose A. Afonso & Sérgio F. Lopes, 2023. "An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks," Future Internet, MDPI, vol. 15(8), pages 1-16, August.
    2. Elias Dritsas & Maria Trigka, 2024. "Machine Learning for Blockchain and IoT Systems in Smart Cities: A Survey," Future Internet, MDPI, vol. 16(9), pages 1-15, September.
    3. Alireza Fath & Nicholas Hanna & Yi Liu & Scott Tanch & Tian Xia & Dryver Huston, 2024. "Indoor Infrastructure Maintenance Framework Using Networked Sensors, Robots, and Augmented Reality Human Interface," Future Internet, MDPI, vol. 16(5), pages 1-23, May.
    4. Imran Moez Khan & Andrew Thompson & Akram Al-Hourani & Kandeepan Sithamparanathan & Wayne S. T. Rowe, 2023. "RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems," Future Internet, MDPI, vol. 15(6), pages 1-17, June.
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