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RELoc: An Enhanced 3D WiFi Fingerprinting Indoor Localization Algorithm with RFECV Feature Selection.

Shehu Lukman Ayinla1,2, Azrina Abd Aziz1, Micheal Drieberg1

  • 1Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia.

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Summary
This summary is machine-generated.

This study introduces RELoc, a 3D indoor localization framework using AI for enhanced WiFi fingerprinting. RELoc improves accuracy in multi-floor environments by overcoming limitations of 2D methods.

Keywords:
Optuna-TPEWiFi fingerprintingextremely randomized trees (ERT)indoor localizationrecursive feature elimination with cross-validation (RFECV)

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Geomatics Engineering

Background:

  • WiFi fingerprinting is crucial for indoor localization but often limited to 2D.
  • Existing 2D methods struggle with vertical ambiguity and spatial modeling in multi-floor environments, reducing reliability.
  • Accurate indoor localization is essential for numerous real-world applications.

Purpose of the Study:

  • To propose RELoc, a novel 3D indoor localization framework addressing the limitations of 2D approaches.
  • To enhance WiFi fingerprinting-based localization accuracy and reliability, particularly in complex multi-floor settings.
  • To integrate advanced machine learning techniques for optimal Access Point selection and precise coordinate regression.

Main Methods:

  • Utilizes Recursive Feature Elimination with Cross-Validation (RFECV) for efficient Access Point (AP) selection.
  • Employs Extremely Randomized Trees (ERT) for accurate 2D and 3D coordinate regression.
  • Optimizes ERT hyperparameters via Bayesian optimization with Optuna's Tree-structured Parzen Estimator (TPE).

Main Results:

  • RELoc achieves superior performance in both 2D and 3D indoor localization on SODIndoorLoc and UTSIndoorLoc datasets.
  • Demonstrates Mean Absolute Errors (MAEs) of 1.84 m (2D, SODIndoorLoc) and 4.39 m (2D, UTSIndoorLoc).
  • Achieves significant improvements (33.15% and 26.88%) when incorporating floor information and outperforms state-of-the-art methods like GNN, DNN, and ET.

Conclusions:

  • The proposed RELoc framework significantly enhances indoor localization accuracy and robustness, especially in multi-floor environments.
  • 3D spatial modeling is critical for achieving spatially discriminative and reliable indoor localization.
  • RELoc offers a promising solution for precise indoor positioning using WiFi fingerprinting and advanced AI.