Abstract
This paper introduces a method for the accurate indoor localization for mobile users when they are surrounded by unknown environments in places like airports, hospitals, libraries, museums, and supermarkets. Our system makes use of the combined data comprising two kinds: indoor Wi-Fi signals and the images of surroundings taken by users. We use Wi-Fi registration based on IEEE 802.11 to determine Access Point location according to the Received Signal Strength (RSS) as a distance function. Our fingerprinting method gives probability of signal strengths histogram at a given location. We use the Received Signal Strength Indicator (RSSI) data in to data collection to determine the overage area estimation and the mode of RSSI in localization. Next, we utilize the Speed Up Robust Features (SURF) descriptor to match the user-captured images with the image repository containing pre-captured images of the environment. Our method is accurate and less time consuming as compared to different approaches.
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
Pahlavan, K., Xinrong, L., Makela, J.P.: Indoor geo-location science and technology. IEEE Communications Magn. 40(2), 112–118 (2002)
Nisarg, K., Balajee, K., Glasgwow, E.D., Dias, M.B.: Bringing Navigation Indoors. The Way We Live Next (2008); Robust Indoor Localization on a Commercial Smart Phone. Procedia Computer Science 10, 1114–1120 (2012)
Van den Berghe, S., Weyn, M.: Fusing Camera and Wi-Fi Sensors for Opportunistic Localization. In: UBICOMM 2011: The Fifth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies IARIA (2011) ISBN: 978-1-61208-171-7
Arai, I., Horimi, S., Nishio, N.: Wi-Foto 2: Heterogeneous device controller using Wi-Fi positioning and template matching. Adjunct Proceedigs of Pervasive (2010)
Zhang, Y., Ma, L., Zhang, R.: A Quick Image Registration Algorithm Based on Delaunay Triangulation. TELKOMNIKA 11(2), 761–773 (2013)
Chandrasekaran, G., Ergin, M.A., Yang, J., Liu, S., Chen, Y., Gruteser, M., Martin, R.P.: Empirical Evaluation of the Limits on Localization Using Signal Strength. In: Proc. SECON 2009, pp. 333–341 (2009)
Bahl, P., Padmanabhan, V.N.: RADAR: An In-Building RF-based User Location and Tracking System. In: Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies 2000, vol. 2, pp. 775–784 (2000)
Haeberlen, A., Flannery, E., Ladd, A.M., Rudys, A., Wallach, D.S., Kavraki, L.E.: Practical Robust Localization over Large Scale 802.11 Wireless Networks. In: Proc. MOBICOM 2004, pp. 70–84 (2004)
Niculescu, D., Nath, B.: Ad hoc positioning system (APS) using AOA. In: Niculescu, D., Nath, B. (eds.) INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications, vol. 3, pp. 1734–1743. IEEE Societies (2003)
Zhang, Y., Brown, A.K., Malik, W.Q., et al.: High resolution 3-D angle of arrival determination for indoor UWB multipath propagation. IEEE Transactions on Wireless Communications 7(8), 3047–3055 (2008)
Llombart, M., Ciurana, M., Barcelo-Arroyo, F.: On the scalability of a novel WLAN positioning system based on time of arrival measurements. In: 5th Workshop on WPNC 2008, pp. 15–21. IEEE (2008)
Han, D., Andersen, D.G., Kaminsky, M., Papagiannaki, K., Seshan, S.: Access point localization using local signal strength gradient. In: Moon, S.B., Teixeira, R., Uhlig, S., et al. (eds.) PAM 2009. LNCS, vol. 5448, pp. 99–108. Springer, Heidelberg (2009)
Gmskaya, H., Hakkoymaz, H.: WiPoD wireless positioning system based on 802.11 WLAN infrastructure. Proceedings of the Enformatika 9, 126–130 (2005)
Bouris, D., Nikitakis, A.: Fast and Efficient FPGA-based Feature Detection employing the SURF Algorithm. In: 18th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines (2010)
Park, J.G., Charrow, B., Curtis, D., Battat, J., Minkov, E., Hicks, J., Teller, S., Ledlie, J.: Growing an organic indoor location system. In: Proc. MobiSys 2010, pp. 271–284 (2010)
El gayar, M.M., Soliman, H., meky, N.: A comparative study of image low level feature extraction algorithms. Proc. Egyptian Informatics Journal 14, 175–181 (2013)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Quan, M., Navarro, E., Peuker, B.: Wi-Fi Localization Using RSSI Fingerprinting, Decertation from California Polytechnic State University (January 2010), http://digitalcommons.calpoly.edu/cpesp/17/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Niu, J., Ramana, K.V., Wang, B., Rodrigues, J.J.P.C. (2014). A Robust Method for Indoor Localization Using Wi-Fi and SURF Based Image Fingerprint Registration. In: Guo, S., Lloret, J., Manzoni, P., Ruehrup, S. (eds) Ad-hoc, Mobile, and Wireless Networks. ADHOC-NOW 2014. Lecture Notes in Computer Science, vol 8487. Springer, Cham. https://doi.org/10.1007/978-3-319-07425-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-07425-2_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07424-5
Online ISBN: 978-3-319-07425-2
eBook Packages: Computer ScienceComputer Science (R0)