Abstract
Indoor Positioning Services (IPS) estimate the location of devices, frequently being mobile terminals, in a building. Many IPS algorithms rely on the Received Signal Strength (RSS) of radio signals observed at a terminal. However, these signals are noisy due to both the impact of the surrounding environment such as presence of other persons, and limited accuracy of strength measurements. Hence, the question arises whether raw RSS data can undergo transformation preserving its information content, but reducing the data and increasing the resilience of the algorithms to inherent noisiness of raw RSS data.
This work evaluates the use of binning applied to RSS data and proposes the way the reduction of RSS data can be attained. Results show that the proposed way of using RSS binning causes little or no positioning accuracy degradation. Still, it yields significant RSS data reduction. Interestingly, in some cases data reduction results even in accuracy gains.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bento, C., Soares, T., Veloso, M., Baptista, B.: A study on the suitability of GSM signatures for indoor location. In: Schiele, B., Dey, A.K., Gellersen, H., de Ruyter, B., Tscheligi, M., Wichert, R., Aarts, E., Buchmann, A.P. (eds.) AmI 2007. LNCS, vol. 4794, pp. 108–123. Springer, Heidelberg (2007)
Buyruk, H., Keskin, A., Sendil, S., Celebi, H., Partal, H., Ileri, O., Zeydan, E., Ergut, S.: RF fingerprinting based GSM indoor localization. In: 2013 21st Signal Processing and Communications Applications Conference (SIU), pp. 1–4, April 2013
Flach, P.: Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge University Press, Cambridge (2012)
Grzenda, M.: On the prediction of floor identification credibility in RSS-based positioning techniques. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds.) IEA/AIE 2013. LNCS, vol. 7906, pp. 610–619. Springer, Heidelberg (2013)
Kjargaard, M.B.: Indoor location fingerprinting with heterogeneous clients. Pervasive Mob. Comput. 7, 31–43 (2011)
Machaj, J., Brida, P.: Performance comparison of similarity measurements for database correlation localization method. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part II. LNCS, vol. 6592, pp. 452–461. Springer, Heidelberg (2011)
Varshavsky, A., de Lara, E., Hightower, J., LaMarca, A., Otsason, V.: GSM indoor localization. Pervasive Mob. Comput. 3(6), 698–720 (2007). perCom 2007
Yang, Q., Pan, S.J., Zheng, V.W.: Estimating location using Wi-Fi. IEEE Intell. Syst. 23(1), 8–13 (2008)
Acknowledgments
This research was supported by the National Centre for Research and Development, grant No PBS2/B3/24/2014, application No 208921.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Grzenda, M. (2015). Reduction of Signal Strength Data for Fingerprinting-Based Indoor Positioning. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-24834-9_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24833-2
Online ISBN: 978-3-319-24834-9
eBook Packages: Computer ScienceComputer Science (R0)