LoRaWAN based indoor localization using random neural networks

Winfred Ingabire, Hadi Larijani*, Ryan M. Gibson, Ayyaz-UI-Haq Qureshi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
3 Downloads (Pure)

Abstract

Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the ultra-high frequency signals. Furthermore, fingerprint localization using Received Signal Strength Indicator (RSSI) values is typical for localization in indoor environments. Therefore, this paper develops a low-power intelligent localization system for indoor environments using Long-Range Wide-Area Networks (LoRaWAN)RSSI values with Random Neural Networks (RNN). The proposed localization system demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization error of 0.12 m in the Line-of-Sight (LOS). The obtained results confirm LoRaWAN-RNN-based localization systems suitable for indoor environments in LOS applied in big sports halls, hospital wards, shopping malls, airports, and many more with the highest accuracy of 99.52%. Furthermore, a minimum average localization error of 13.94 m was obtained in the Non-Line-of-Sight (NLOS) scenario, and this result is appropriate for the management and control of vehicles in indoor carparks, industries, or any other fleet in a pre-defined area in the NLOS with the highest accuracy of 44.24%.

Original languageEnglish
Article number303
Number of pages11
JournalInformation (Switzerland)
Volume13
Issue number6
DOIs
Publication statusPublished - 16 Jun 2022

Keywords

  • IoT
  • LoRaWAN
  • RSSI
  • indoor localization
  • RNN

ASJC Scopus subject areas

  • Information Systems

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