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Locations Selection for Periodic Radio Map Update in WiFi Fingerprinting

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

The construction and update of a radio map are usually referred as the main drawbacks of WiFi fingerprinting, a very popular method in indoor localization research. For radio map update, some studies suggest taking new measurements at some random locations, usually from the ones used in the radio map construction. In this paper, we argue that the locations should not be random, and propose how to determine them. Given the set locations where the measurements used for the initial radio map construction were taken, a subset of locations for the update measurements is chosen through optimization so that the remaining locations found in the initial measurements are best approximated through regression. The regression method is Support Vector Regression (SVR) and the optimization is achieved using a genetic algorithm approach. We tested our approach using a database of WiFi measurements collected at a relatively dense set of locations during ten months in a university library setting. The experiments results show that, if no dramatic event occurs (e.g., relevant WiFi networks are changed), our approach outperforms other strategies for determining the collection locations for periodic updates. We also present a clear guide on how to conduct the radio map updates.

Keywords

  • Wifi fingerprinting
  • Radio map update
  • Regression
  • Optimization Genetic algorithm

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  • DOI: 10.1007/978-3-319-71470-7_1
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Acknowledgements

Germán M. Mendoza-Silva gratefully acknowledges funding from grant PREDOC/2016/55 by Universitat Jaume I.

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Correspondence to Germán M. Mendoza-Silva .

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Mendoza-Silva, G.M., Torres-Sospedra, J., Huerta, J. (2018). Locations Selection for Periodic Radio Map Update in WiFi Fingerprinting. In: Kiefer, P., Huang, H., Van de Weghe, N., Raubal, M. (eds) Progress in Location Based Services 2018. LBS 2018. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-71470-7_1

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