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FABEL: feature association based ensemble learning for positioning in indoor environment

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Abstract

Optimal selection of features leads to the increase in perceptibility of the predictor procedure and thereby in turn increases the accuracy. In the domain of WiFi-based indoor positioning, access points are a ubiquitous source of features with which the positioning process is carried out. The selection of a proper subset of access points is crucial for sustainable localization performance. Most of the existing works in the literature focus on important AP selection whereas, the stability of the feature sets w.r.t varying context remains mostly ignored. Thus, to impart sustainable localization performance, in this work, the problem of identification of a stable set(s) of APs is investigated. Thus, the contribution of this work is two-fold. A genetic algorithm based feature selection technique for indoor localization is proposed first. The motive behind the approach is to capture different subsets of APs that best contribute to the localization process. Accordingly, our second contribution is the design of a Feature Association Based Ensemble Learning (FABEL) model that utilizes the selected feature sets in order to retain generality for sustainable localization performance.

Experimentation has been carried out on real-world data and analysis has been presented. The proposed training pipeline has been tested for device heterogeneity. It has been found that even for an unknown test device, the localization accuracy is significantly good and 80% of the errors lies within 4m.

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Acknowledgements

This research work is partially supported by the project entitled- “Developing Framework for Indoor Location Based Services with Seamless Indoor Outdoor Navigation by expanding Spatial Data Infrastructure”, funded by the Ministry of Science and Technology, Department of Science and Technology, NGP Division, Government of India, ref no. NRDMS/UG/NetworkingProject/e-13/2019(G).

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Authors and Affiliations

  1. Jadavpur University, Kolkata, West Bengal, India

    Satyam Parsuramka

  2. Computer Science and Engineering, Institute of Engineering & Management, Jadavpur University, Kolkata, West Bengal, India

    Ayan Kumar Panja

  3. Sister Nivedita University, New Town, West Bengal, 700156, India

    Priya Roy

  4. Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Sarmistha Neogy & Chandreyee Chowdhury

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  1. Satyam Parsuramka
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  2. Ayan Kumar Panja
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  3. Priya Roy
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  4. Sarmistha Neogy
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  5. Chandreyee Chowdhury
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Correspondence to Chandreyee Chowdhury.

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Parsuramka, S., Panja, A.K., Roy, P. et al. FABEL: feature association based ensemble learning for positioning in indoor environment. Multimed Tools Appl 82, 7247–7266 (2023). https://doi.org/10.1007/s11042-022-13651-z

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  • DOI: https://doi.org/10.1007/s11042-022-13651-z

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