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Wi-Fi Fingerprint-Based Indoor Localization Method via Standard Particle Swarm Optimization.

Jin Zheng1, Kailong Li2, Xing Zhang2

  • 1School of Architecture and Art, Central South University, Changsha 410083, China.

Sensors (Basel, Switzerland)
|July 9, 2022
PubMed
Summary

This study enhances Wi-Fi fingerprint indoor localization using particle swarm optimization and a novel homogeneity model. The improved method significantly boosts localization accuracy compared to traditional algorithms.

Keywords:
Wi-Fi fingerprintindoor localizationlocation estimationparticle swarm optimization

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Indoor localization is crucial for Internet-of-Things (IoT) applications.
  • Wi-Fi fingerprinting is a popular indoor localization technique.
  • High-accuracy localization using Wi-Fi fingerprints remains a challenge.

Purpose of the Study:

  • To propose an improved Wi-Fi fingerprint-based indoor localization method.
  • To enhance localization accuracy by applying particle swarm optimization (PSO).
  • To introduce a new two-panel fingerprint homogeneity model for better similarity characterization.

Main Methods:

  • Application of the standard particle swarm optimization algorithm.
  • Development and implementation of a two-panel fingerprint homogeneity model.
  • Experimental verification of the proposed localization method's performance.

Main Results:

  • The proposed method demonstrates superior performance compared to conventional algorithms.
  • Significant improvements in localization accuracy were observed: 15.32% (KNN), 15.91% (SVM), 32.38% (LR), and 36.64% (RF).

Conclusions:

  • The proposed PSO-based Wi-Fi fingerprint localization with a homogeneity model effectively improves accuracy.
  • This approach offers a promising solution for high-accuracy indoor localization in IoT environments.