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A Semi-Supervised Transfer Learning with Grid Segmentation for Outdoor Localization over LoRaWans.

Yuh-Shyan Chen1, Chih-Shun Hsu2, Chan-Yin Huang1

  • 1Department of Computer Science and Information Engineering, National Taipei University, No. 151, University Rd., San Shia District, New Taipei City 23741, Taiwan.

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Summary
This summary is machine-generated.

This study introduces a novel outdoor localization scheme for LoRaWANs using semi-supervised transfer learning. The method enhances location accuracy by generating virtual labeled data, outperforming existing outdoor localization techniques.

Keywords:
LoRaWANdeep learninginternet of thing (IoT)outdoor localizationsemi-supervised learning

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Area of Science:

  • Wireless communication
  • Machine learning
  • Geospatial analysis

Background:

  • Collecting labeled outdoor data for localization is challenging and costly.
  • Semi-supervised transfer learning offers a solution by leveraging limited labeled data.
  • Existing outdoor localization methods often struggle with accuracy and error reduction.

Purpose of the Study:

  • To propose a new outdoor localization scheme for LoRaWANs.
  • To improve location accuracy and reduce errors in outdoor environments.
  • To utilize semi-supervised transfer learning for efficient data utilization.

Main Methods:

  • Proposed a semi-supervised transfer learning approach for LoRaWAN localization.
  • Implemented a grid segmentation concept to generate virtual labeled data.
  • Utilized signal features like RSSI, SNR, and timestamps for training.

Main Results:

  • The proposed scheme significantly improves location accuracy.
  • Demonstrated a reduction in localization error compared to existing methods.
  • The generation of virtual labeled data positively correlates with accuracy.

Conclusions:

  • Semi-supervised transfer learning is effective for outdoor LoRaWAN localization.
  • The proposed grid segmentation and virtual data generation enhance accuracy.
  • The method offers a practical solution for accurate outdoor positioning.