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Designing an ensemble of classifiers for smartphone-based indoor localization irrespective of device configuration

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Abstract

WiFi-based indoor localization is a popular approach as most buildings and campuses are WiFi-enabled and its fingerprints are captured by smartphones carried by every individual. Due to the different WiFi sensitivity of the smartphones, an interesting challenge subject to varying ambient conditions emerges in this domain. Thus, a single supervised classifier may not be able to provide stable localization accuracy when the devices used for training and testing are different. Accordingly, a more generalized Neural Network-based Ensemble Learning for Indoor Localization System (NNELILS) is designed in this paper to address the challenge regarding device heterogeneity. NNELILS has a heterogeneous set of base classifiers and a Neural Network-based meta-classifier that combines the decisions of base classifiers. Accordingly, algorithms are proposed and implemented on real-life datasets. Our proposed system is found to improve the localization accuracy to 94% when the training and testing devices vary. It is even found to work better than the state-of-the-art approaches.

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Notes

  1. https://drive.google.com/drive/folders/1_z1qhoRIcpineP9AHkfVGCfB2Fd_e-fD

  2. https://archive.ics.uci.edu/ml/datasets/UJIIndoorLoc

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Acknowledgements

This research work is supported by the State Government Fellowship scheme of Jadavpur University funded by Government of West Bengal, India and 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). We would like to thank Mr. Mausam Kundu for his cooperation. We would also like to thank the anonymous reviewers and the editor for considering our manuscript and providing valuable reviews which has greatly enhanced the quality of the paper.

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  1. Computer Science and Engineering Department, Jadavpur University, Kolkata, India

    Priya Roy & Chandreyee Chowdhury

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  1. Priya Roy
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Correspondence to Chandreyee Chowdhury.

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Roy, P., Chowdhury, C. Designing an ensemble of classifiers for smartphone-based indoor localization irrespective of device configuration. Multimed Tools Appl 80, 20501–20525 (2021). https://doi.org/10.1007/s11042-020-10456-w

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