Outdoor node localization using random neural networks for large-scale urban IoT LoRa networks

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)
45 Downloads (Pure)


Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.

Original languageEnglish
Article number307
Number of pages13
Issue number11
Early online date23 Oct 2021
Publication statusPublished - Nov 2021


  • IoT
  • LoRaWAN
  • RSSI
  • localization
  • RNN

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Numerical Analysis
  • Computational Theory and Mathematics
  • Computational Mathematics


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