Abstract
In this work, we provide an analysis of BLE channel-separate fingerprinting using different distance and similarity measures. In a 168 m\(^{2}\) testbed, 12 beacons with Eddystone and iBeacon protocols set were deployed, taking into account the orientation of users and considering 10 distance/similarity measures. We have observed that there is an orientation that offers the best positioning performance with the combination of iBeacon protocol, channel 38 and Mahalanobis distance. Taking 8 samples in the online phase, accuracy values obtained are in the range 1.28 m–1.88 m, and precision values are within 1.90 m–3.76 m or less, 90% of the time and depending which orientation the observer is facing.
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
References
Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Proceedings of IEEE INFOCOM, the Conference on Computer Communications, Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, Reaching the Promised Land of Communications, Tel Aviv, Israel, pp. 775–784, 26–30 March 2000
de Blasio, G., Quesada-Arencibia, A., García, C.R., Molina-Gil, J.M., Caballero-Gil, C.: Study on an indoor positioning system for harsh environments based on Wi-Fi and bluetooth low energy. Sensors 17(6), 27692–27720 (2017)
Bluetooth Special Interest Group: Bluetooth 5—Bluetooth technology website. https://www.bluetooth.com/specifications/bluetooth-core-specification/bluetooth5/. Accessed 1 Mar 2017
Caso, G., de Nardis, L., di Benedetto, M.G.: A mixed approach to similarity metric selection in affinity propagation-based WiFi fingerprinting indoor positioning. Sensors 15(11), 27692–27720 (2015). http://www.mdpi.com/1424-8220/15/11/27692
Cha, S.H.: Comprehensive survey on distance/similarity measures between probability density functions. Int. J. Math. Models Methods Appl. Sci. 1(4), 300–307 (2007)
Faragher, R., Harle, R.: Location fingerprinting with Bluetooth low energy beacons. IEEE J. Sel. Areas Commun. 33(11), 2418–2428 (2015)
Fard, H.K., Chen, Y., Son, K.K.: Indoor positioning of mobile devices with agile iBeacon deployment. In: CCECE, pp. 275–279. IEEE (2015)
Hossain, A.K.M.M., Jin, Y., Soh, W.S., Van, H.N.: SSD: a robust RF location fingerprint addressing mobile devices’ heterogeneity. IEEE Trans. Mob. Comput. 12(1), 65–77 (2013)
Ishida, S., Takashima, Y., Tagashira, S., Fukuda, A.: Proposal of separate channel fingerprinting using Bluetooth low energy. In: 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 230–233, July 2016
Liu, H., Darabi, H., Banerjee, P.P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. Part C 37(6), 1067–1080 (2007)
Moghtadaiee, V., Dempster, A.G.: Determining the best vector distance measure for use in location fingerprinting. Pervasive Mob. Comput. 23, 59–79 (2015)
Peng, Y., Fan, W., Dong, X., Zhang, X.: An iterative weighted KNN (IW-KNN) based indoor localization method in bluetooth low energy (BLE) environment. In: 2016 International IEEE Conferences on Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp. 794–800, July 2016
Torres-Sospedra, J., Montoliu, R., Trilles, S., Belmonte, O., Huerta, J.: Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems. Expert Syst. Appl. 42, 9263–9278 (2015)
Čabarkapa, D., Grujić, I., Pavlović, P.: Comparative analysis of the Bluetooth low-energy indoor positioning systems. In: 2015 12th International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services (TELSIKS), pp. 76–79, October 2015
Zhuang, Y., Yang, J., Li, J., Qi, L., El-Sheimy, N.: Smartphone-based indoor localization with Bluetooth low energy beacons. Sensors 16, 596 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
de Blasio, G., Quesada-Arencibia, A., García, C.R., Moreno-Díaz, R., Rodríguez-Rodríguez, J.C. (2017). Analysis of Distance and Similarity Metrics in Indoor Positioning Based on Bluetooth Low Energy. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-67585-5_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67584-8
Online ISBN: 978-3-319-67585-5
eBook Packages: Computer ScienceComputer Science (R0)