Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Indoor Localization Internet of Things Zigbee Fingerprint Technique Fingerprint Database Interpolation Regression Polynomial.

Authors

  • Dwi Joko Suroso School of Engineering, King Mongkut's Institute of Technology Ladkrabang, 1, Soi Chalongkrung 1, Bangkok 10520,, Thailand https://orcid.org/0000-0003-3418-7259
  • Farid Yuli Martin Adiyatma Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Jl. Grafika No. 2, Yogyakarta 55281,, Indonesia
  • Panarat Cherntanomwong
    panarat.ch@kmitl.ac.th
    School of Engineering, King Mongkut's Institute of Technology Ladkrabang, 1, Soi Chalongkrung 1, Bangkok 10520,, Thailand
  • Pitikhate Sooraksa School of Engineering, King Mongkut's Institute of Technology Ladkrabang, 1, Soi Chalongkrung 1, Bangkok 10520,, Thailand

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Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden.

 

Doi: 10.28991/esj-2021-SP1-012

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