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C-VoNNI: a precise fingerprint construction for indoor positioning systems using natural neighbor methods with clustering-based Voronoi diagrams

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Abstract

Indoor positioning is crucial for everyday life, and received signal strength-based fingerprint localization is the most effective method. However, updating the fingerprint database is laborious, as changes in indoor layout would render the initial radio map outdated. To address this issue, we propose a precise radio map construction method by clustering and interpolating virtual fingerprints. The affinity propagation clustering algorithm and Voronoi diagram are used to group fingerprints with similar characteristics, mitigating the negative effects of multipath fading and shadowing caused by changes in the indoor layout. After generating synthetic reference points using the gradient extrapolation method to expand the convex hull, natural neighbor interpolation can construct accurate virtual fingerprints. Experimental results show that our proposed method outperformed both inverse distance weighting and Kriging interpolation by up to 33% in localization accuracy across diverse environments. This approach enables efficient radio map generation with comparable localization accuracy to the original radio map without extensive site surveys.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme (FRGS) with Grant Number FRGS/1/2019/ICT02/MMU/02/1.

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Author notes

  1. Chee Keong Tan, Ian K. T. Tan and Su Wei Tan have contributed equally to this work.

Authors and Affiliations

  1. School of Information Technology, Monash University, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia

    Yun Fen Yong, Chee Keong Tan & Su Wei Tan

  2. School of Mathematical and Computer Sciences, Heriot Watt University, 62200, Putrajaya, W. P. Putrajaya, Malaysia

    Ian K. T. Tan

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  1. Yun Fen Yong
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Contributions

YFY: constructed the model and conceived the algorithm; CKT: analyzed the data and provides assistance during the design process. All authors contributed to the revisions of this manuscript. All authors have read and approved the final manuscript.

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Correspondence to Yun Fen Yong.

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Yong, Y.F., Tan, C.K., Tan, I.K.T. et al. C-VoNNI: a precise fingerprint construction for indoor positioning systems using natural neighbor methods with clustering-based Voronoi diagrams. J Supercomput 80, 10667–10694 (2024). https://doi.org/10.1007/s11227-023-05855-3

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