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DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier.

Imran Ashraf1, Soojung Hur1, Sangjoon Park2

  • 1Department of Information & Communication Engineering, Yeungnam University, Gyeongbuk, Gyeongsan-si 38541, Korea.

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Summary
This summary is machine-generated.

This study uses deep neural networks to improve indoor localization accuracy across different smartphones. The approach mitigates device variations, achieving consistent results for location-based services.

Keywords:
deep learningensemble classifierfeature extractionindoor localizationmagnetic fieldneural networkssmartphone sensors

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Area of Science:

  • Indoor positioning and localization
  • Location-based services
  • Machine learning for spatial computing

Background:

  • Growing demand for indoor positioning services necessitates accurate localization techniques.
  • Wi-Fi fingerprinting faces signal strength variation issues; magnetic field localization shows promise but suffers from device heterogeneity.
  • Existing localization methods exhibit performance differences across various smartphone models, impacting accuracy.

Purpose of the Study:

  • To develop a deep neural network-based ensemble classifier for indoor localization using magnetic field data.
  • To devise an approach that achieves similar localization accuracy across heterogeneous devices (smartphones).
  • To investigate and mitigate the impact of device dependence on indoor localization accuracy.

Main Methods:

  • Utilized magnetic field data from smartphones for indoor localization.
  • Employed deep neural networks (NNs) and an ensemble classifier for training and localization.
  • Experimented with multiple smartphone models (Galaxy S8, LG G6, LG G7, Galaxy A8) and a public dataset (Sony Xperia M2).

Main Results:

  • The proposed deep neural network approach significantly mitigates device heterogeneity in indoor localization.
  • Achieved a localization accuracy of 2.64 m at 50% across four different devices.
  • Demonstrated mean errors of 2.23 m (Galaxy S8) to 2.78 m (Galaxy A8) and 2.84 m on a public dataset.

Conclusions:

  • Deep neural networks are effective in enhancing indoor localization accuracy and reducing device-specific performance variations.
  • The developed method offers a promising solution for consistent and accurate indoor positioning across diverse smartphone hardware.
  • Further investigation into device attitudes confirmed their impact on localization accuracy, suggesting avenues for future research.