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Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in

Safar Maghdid Asaad1,2, Halgurd Sarhang Maghdid2

  • 1Department of Technical Information Systems Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq.

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Summary

This study introduces Norm_MSATE_LSTM, a novel algorithm to improve indoor Wi-Fi positioning accuracy. The method enhances Received Signal Strength Indicator (RSSI) data, significantly boosting positioning performance for Internet-of-Things (IoT) applications.

Keywords:
AugmentationDeep learningIndoors localizationIndoors positioningLSTMMachine learningOMNeT++RSSI-based fingerprintWSN

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Ubiquitous Computing

Background:

  • Internet-of-Things (IoT) devices increasingly track indoor activities using Wi-Fi.
  • Received Signal Strength Indicator (RSSI) based indoor positioning faces challenges like multipath, non-line-of-sight (NLOS) issues, and data fluctuations.
  • Existing fingerprinting methods struggle with insufficient data samples and unstable matching, especially in complex environments.

Purpose of the Study:

  • To propose a novel matching algorithm, Norm_MSATE_LSTM, to enhance indoor Wi-Fi positioning accuracy.
  • To address the limitations of RSSI-based fingerprinting, including data scarcity and matching instability.
  • To improve the reliability and precision of indoor localization for IoT applications.

Main Methods:

  • Implemented a Mean Standard deviation Augmentation TEchnique (MSATE) for data augmentation to increase RSSI records.
  • Applied data normalization (norm) to RSSI records.
  • Utilized Long Short-Term Memory (LSTM) networks for position estimation.
  • Compared the proposed Norm_MSATE_LSTM algorithm against Weighted k-Nearest Neighbors (WkNN) and standalone LSTM.

Main Results:

  • The proposed Norm_MSATE_LSTM algorithm demonstrated significant improvements in positioning accuracy.
  • Accuracy improved by 33.1% with augmentation alone.
  • Accuracy improved by 57.5% with augmentation and normalization combined.
  • Experimental and simulated results using OMNeT++ validated the algorithm's effectiveness.

Conclusions:

  • The Norm_MSATE_LSTM algorithm effectively mitigates issues in RSSI-based fingerprinting for indoor positioning.
  • Data augmentation and normalization, combined with LSTM, substantially enhance localization accuracy.
  • The proposed method offers a promising solution for reliable indoor tracking in IoT systems.