Abstract
Currently the most accurate WLAN positioning systems are based on the fingerprinting approach, where a “radio map” is constructed by modeling how the signal strength measurements vary according to the location. However, collecting a sufficient amount of location-tagged training data is a rather tedious and time consuming task, especially in indoor scenarios — the main application area of WLAN positioning — where GPS coverage is unavailable. To alleviate this problem, we present a semi-supervised manifold learning technique for building accurate radio maps from partially labeled data, where only a small portion of the signal strength measurements need to be tagged with the corresponding coordinates. The basic idea is to construct a non-linear projection that maps high-dimensional signal fingerprints onto a two-dimensional manifold, thereby dramatically reducing the need of location-tagged data. Our results from a deployment in a real-world experiment demonstrate the practical utility of the method.
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
Bahl, P., Padmanabhan, V.N.: RADAR: An In-building RF-based User Location and Tracking system. In: 19th Conference of IEEE Computer and Communications Societies, pp. 775–784. IEEE Computer Society, Piscataway (2000)
Belkin, M., Niyogi, P., Sindhwani, V.: On Manifold Regularization. In: 10th International Workshop on Artificial Intelligence and Statistics, pp. 17–24. IOS Press, Amsterdam (2005)
Cox, T.F., Cox, M.A.A.: Multidimensional Scaling. Chapman & Hall, London (2001)
Dedes, G., Dempster, A.: Indoor GPS: Positioning Challenges and Opportunities. In: 62nd Vehicular Technology Conference, pp. 412–415. IEEE Press, Piscataway (2005)
Ekahau, Inc. RTLS, http://www.ekahau.com
Ferris, B., Hahnel, D., Fox, D.: Gaussian Processes for Signal Strength-Based Location Estimation. In: Robotics: Science and Systems, pp. 1–8. MIT Press, Cambridge (2006)
Ghodsi, A.: Dimensionality Reduction – A Short Tutorial. Technical report. University of Waterloo (2006)
Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of Wireless Indoor Positioning Techniques and Systems. IEEE T. Syst. Man. Cyb. 37, 1067–1080 (2007)
Ni, L.M., Liu, Y., Lau, Y.C., Patil, A.P.: LANDMARC: Indoor Location Sensing Using Active RFID. Wireless Networks 10, 701–710 (2004)
Pan, J.J., Yang, Q., Chang, H., Yeung, D.-Y.: A Manifold Regularization Approach to Calibration Reduction for Sensor-Network Based Tracking. In: 21st National Conference on Artificial Intelligence, pp. 988–993. AAAI Press, Menlo Park (2006)
Pan, J.J., Yang, Q.: Co-localization from Labeled and Unlabeled Data Using Graph Laplacian. In: 20th International Joint Conference on Artificial Intelligence, pp. 2166–2171. Morgan Kaufmann, San Francisco (2007)
Papapostolou, A., Chaouchi, H.: WIFE: Wireless Indoor Positioning Based on Fingerprint Evaluation. In: Fratta, L., Schulzrinne, H., Takahashi, Y., Spaniol, O. (eds.) NETWORKING 2009. LNCS, vol. 5550, pp. 234–247. Springer, Heidelberg (2009)
Patwari, N., Hero, A.O.: Adaptive Neighborhoods for Manifold Learning-based Sensor Localization. In: IEEE 6th Workshop on Signal Processing Advances in Wireless Communications, pp. 1098–1102. IEEE Press, Piscataway (2005)
Petrellis, N., Konofaos, N., Alexiou, G.: A Wireless Infrared Sensor Network for the Estimation of the Position and Orientation of a Moving Target. In: Third International Mobile Multimedia Communications Conference, pp. 1–4. ICST, Brussels (2007)
Raper, J., Gartner, G., Karimi, H., Rizos, C.: Applications of Location-based Services: A Selected Review. J. Location-based Services 1, 89–111 (2007)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2005)
Roos, T., Myllymäki, P., Tirri, H., Misikangas, P., Sievänen, J.: A Probabilistic Approach to WLAN User Location Estimation. Int. J. Wireless Information Networks 9, 155–164 (2002)
Samko, O., Marshall, A.D., Rosin, P.L.: Selection of the Optimal Parameter Value for the Isomap Algorithm. Pattern Recogn. Letters 27, 968–979 (2006)
Shang, Y., Ruml, W., Zhang, Y., Fromherz, M.P.J.: Localization from Mere Connectivity. In: 4th ACM International Symposium on Mobile Ad Hoc Networking & Computing, pp. 201–212. ACM Press, New York (2003)
Shao, C., Huang, H., Wan, C.: Selection of the Suitable Neighborhood Size for the Isomap Algorithm. In: International Joint Conference on Neural Networks, pp. 300–305. IEEE Press, Piscataway (2007)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)
Yeung, W.H., Zhou, J.-Y., Ng, J.K.: Enhanced Fingerprint-Based Location Estimation System in Wireless LAN Environment. In: Denko, M.K., Shih, C.-s., Li, K.-C., Tsao, S.-L., Zeng, Q.-A., Park, S.H., Ko, Y.-B., Hung, S.-H., Park, J.-H. (eds.) EUC-WS 2007. LNCS, vol. 4809, pp. 273–284. Springer, Heidelberg (2007)
Youssef, M., Agrawala, A., Shankar, A.U.: WLAN Location Determination via Clustering and Probability Distributions. In: 1st IEEE International Conference on Pervasive Computing and Communications, pp. 1–8. IEEE Press, Piscataway (2003)
Yuan, J., Li, Y., Liu, C., Zha, X.F.: Leave-One-Out Cross-Validation Based Model Selection for Manifold Regularization. In: Yuan, J., Li, Y.-M., Liu, C.-L., Zha, X.F. (eds.) ISNN 2010. LNCS, vol. 6064, pp. 457–464. Springer, Heidelberg (2010)
Zhang, M., Zhang, S.: An Accurate and Fast WLAN User Location Estimation Method Based on Received Signal Strength. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4489, pp. 58–65. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pulkkinen, T., Roos, T., Myllymäki, P. (2011). Semi-supervised Learning for WLAN Positioning. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21735-7_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-21735-7_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21734-0
Online ISBN: 978-3-642-21735-7
eBook Packages: Computer ScienceComputer Science (R0)