Abstract
Indoor localisation systems based on a Wi–Fi local area wireless technology bring constantly improving results. However, the whole localisation system may fail when one or more Access Point (AP) malfunctions. In this paper we present how to limit the number of observed APs and how to create a malfunction immune localisation method. The presented solutions are an ensemble of random forests with an additional malfunction detection system. The proposed solution reduces a growth of the localisation error to 4 percent for the floor detection inside a six floor building and 2 metres for the horizontal detection in case of a gross malfunction of an AP infrastructure. The system without proposed improvements may give the errors greater than 30 percent and 7 metres respectively in case of not detected changes in the AP’s infrastructure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
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). http://dx.doi.org/10.1007/978-3-642-38577-3_63
Karwowski, J., Okulewicz, M., Legierski, J.: Application of particle swarm optimization algorithm to neural network training process in the localization of the mobile terminal. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds.) EANN 2013, Part I. CCIS, vol. 383, pp. 122–131. Springer, Heidelberg (2013). http://dx.doi.org/10.1007/978-3-642-41013-0_13
Korbel, P., Wawrzyniak, P., Grabowski, S., Krasinska, D.: Locfusion api - programming interface for accurate multi-source mobile terminal positioning. In: 2013 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 819–823, September 2013
Papapostolou, A., Chaouchi, H.: Scene analysis indoor positioning enhancements. Annales des Télécommunications 66, 519–533 (2011)
Roos, T., Myllymaki, P., Tirri, H., Misikangas, P., Sievanen, J.: A probabilistic approach to wlan user location estimation. International Journal of Wireless Information Networks 9(3), 155–164 (2002)
Wang, J., Hu, A., Liu, C., Li, X.: A floor-map-aided wifi/pseudo-odometry integration algorithm for an indoor positioning system. Sensors 15(4), 7096 (2015). http://www.mdpi.com/1424-8220/15/4/7096
Xiang, Z., Song, S., Chen, J., Wang, H., Huang, J., Gao, X.G.: A wireless lan-based indoor positioning technology. IBM Journal of Research and Development 48(5–6), 617–626 (2004)
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
Górak, R., Luckner, M. (2015). Malfunction Immune Wi–Fi Localisation Method. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9329. Springer, Cham. https://doi.org/10.1007/978-3-319-24069-5_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-24069-5_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24068-8
Online ISBN: 978-3-319-24069-5
eBook Packages: Computer ScienceComputer Science (R0)