The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.03 - March (2012 vol.34)
pp: 587-600
Sinno Jialin Pan , Institute for Infocom Research, Singapore
Jie Yin , CSIRO ICT Centre, Marsfield
Jeffrey Junfeng Pan , Facebook, Inc., Palo Alto
Qiang Yang , Hong Kong University of Science and Technology, Hong Kong
ABSTRACT
Recent years have witnessed the growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labeled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters or access points. To solve this problem, we have developed a novel machine learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile users' locations and the locations of access points. Our framework exploits both labeled and unlabeled data from mobile devices and access points. In our two--phase solution, we first build a manifold-based model from a batch of labeled and unlabeled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase colocalization to an online and incremental model that can deal with labeled and unlabeled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed on three different testbeds.
INDEX TERMS
Wireless sensor networks, semi-supervised learning, indoor localization, colocalization, AI applications.
CITATION
Sinno Jialin Pan, Jie Yin, Jeffrey Junfeng Pan, Qiang Yang, "Tracking Mobile Users in Wireless Networks via Semi-Supervised Colocalization", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 3, pp. 587-600, March 2012, doi:10.1109/TPAMI.2011.165
REFERENCES
[1] L. Liao, D.J. Patterson, D. Fox, and H.A. Kautz, “Learning and Inferring Transportation Routines,” Artificial Intelligence, vol. 171, nos. 5/6, pp. 311-331, 2007.
[2] Y. Zheng and X. Xie, “Learning Travel Recommendations from User-Generated GPS Traces,” ACM Trans. Intelligent Systems and Technology, vol. 2, pp. 2:1-2:29, Jan. 2011.
[3] K. Farrahi and D. Gatica-Perez, “Discovering Routines from Large-Scale Human Locations Using Probabilistic Topic Models,” ACM Trans. Intelligent Systems and Technology, vol. 2, pp. 3:1-3:27, Jan. 2011.
[4] M.A. Batalin, G.S. Sukhatme, and M. Hattig, “Mobile Robot Navigation Using a Sensor Network,” Proc. IEEE Int'l Conf. Robotics and Automation, pp. 636-642, Apr. 2004.
[5] S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics. MIT Press, Sept. 2005.
[6] A. Kotanen, M. Hannikainen, H. Leppakoski, and T.D. Hamalainen, “Positioning with IEEE 802.11b Wireless LAN,” Proc. 14th IEEE Indoor and Mobile Radio Comm., pp. 2218-2222, Sept. 2003.
[7] Q. Yang, S.J. Pan, and V.W. Zheng, “Estimating Location Using Wi-Fi,” IEEE Intelligent Systems, vol. 23, no. 1, pp. 8-13, Jan./Feb. 2008.
[8] D. Maligan, E. Elnahrawy, R. Martin, W. Ju, P. Krishnan, and A.S. Krishnakumar, “Bayesian Indoor Positioning Systems,” Proc. IEEE INFOCOM, pp. 1217-1227, Mar. 2005.
[9] P. Bahl and V. Padmanabhan, “RADAR: An In-Building RF-Based User Location and Tracking System,” Proc. IEEE INFOCOM, vol. 2, pp. 775-784, Mar. 2000.
[10] B. Ferris, D. Hahnel, and D. Fox, “Gaussian Processes for Signal Strength-Based Location Estimation,” Proc. Robotics: Science and Systems, Aug. 2006.
[11] X. Nguyen, M.I. Jordan, and B. Sinopoli, “A Kernel-Based Learning Approach to Ad Hoc Sensor Network Localization,” ACM Trans. Sensor Networks, vol. 1, no. 1, pp. 134-152, 2005.
[12] S.C. Deerwester, S.T. Dumais, T.K. Landauer, G.W. Furnas, and R.A. Harshman, “Indexing by Latent Semantic Analysis,” J. Am. Soc. Information Science, vol. 41, no. 6, pp. 391-407, 1990.
[13] M. Belkin and P. Niyogi, “Laplacian Eigenmaps for Dimensionality Reduction and Data Representation,” Neural Computation, vol. 15, no. 6, pp. 1373-1396, 2003.
[14] J. Ham, D. Lee, and L. Saul, “Semisupervised Alignment of Manifolds,” Proc. 10th Int'l Workshop Artificial Intelligence and Statistics, pp. 120-127, Jan. 2005.
[15] J.J. Pan and Q. Yang, “Co-Localization from Labeled and Unlabeled Data Using Graph Laplacian,” Proc. 20th Int'l Joint Conf. Artificial Intelligence, pp. 2166-2171, 2007.
[16] M.H.C. Law and A.K. Jain, “Incremental Nonlinear Dimensionality Reduction by Manifold Learning,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 377-391, Mar. 2006.
[17] O. Kouropteva, O. Okun, and M. Pietikainen, “Incremental Locally Linear Embedding Algorithm,” Pattern Recognition, vol. 38, no. 10, pp. 1764-1767, Oct. 2005.
[18] J.J. Pan, Q. Yang, and S.J. Pan, “Online Co-Localization in Indoor Wireless Networks by Dimension Reduction,” Proc. 22nd Nat'l Conf. Artificial Intelligence, pp. 1102-1107, 2007.
[19] B. Ferris, D. Fox, and N. Lawrence, “WiFi-SLAM Using Gaussian Process Latent Variable Models,” Proc. 20th Int'l Joint Conf. Artificial Intelligence, pp. 2480-2485, 2007.
[20] T. Yairi, “Map Building without Localization by Dimensionality Reduction Techniques,” Proc. 24th Int'l Conf. Machine Learning, pp. 1071-1078, 2007.
[21] E. Foxlin, “Pedestrian Tracking with Shoe-Mounted Inertial Sensors,” IEEE Computer Graphics and Applications, vol. 25, no. 6, pp. 38-46, Nov./Dec. 2005.
[22] M. Bowling, A. Ghodsi, and D. Wilkinson, “Action Respecting Embedding,” Proc. 22nd Int'l Conf. Machine Learning, pp. 65-72, 2005.
[23] J. Letchner, D. Fox, and A. LaMarca, “Large-Scale Localization from Wireless Signal Strength,” Proc. 20th Nat'l Conf. Artificial Intelligence, pp. 15-20, July 2005.
[24] M. Youssef, A. Agrawala, and U. Shankar, “WLAN Location Determination via Clustering and Probability Distributions,” Proc. First IEEE Int'l Conf. Pervasive Computing and Comm., pp. 143-150, Mar. 2003.
[25] S.J. Pan and Q. Yang, “A Survey on Transfer Learning,” IEEE Trans. Knowledge and Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.
[26] J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge Univ. Press, 2004.
[27] M. Belkin, P. Niyogi, and V. Sindhwani, “Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples,” J. Machine Learning Research, vol. 7, pp. 2399-2434, Nov. 2006.
[28] J.J. Pan, Q. Yang, H. Chang, and D.Y. Yeung, “A Manifold Regularization Approach to Calibration Reduction for Sensor-Network Based Tracking,” Proc. 21st Nat'l Conf. Artificial Intelligence, pp. 988-993, July 2006.
[29] F. Chung, Spectral Graph Theory. Am. Math. Soc., 1997.
[30] J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000.
[31] I.S. Dhillon, “Co-Clustering Documents and Words Using Bipartite Spectral Graph Partitioning,” Proc. Seventh ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 269-274, 2001.
[32] B. Hendrickson, “Latent Semantic Analysis and Fiedler Embeddings,” Proc. SIAM Workshop Text Mining, Apr. 2006.
[33] X. Zhu, Z. Ghahramani, and J.D. Lafferty, “Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions,” Proc. 20th Int'l Conf. Machine Learning, pp. 912-919, Aug. 2003.
[34] N. Ravi, N. Dandekar, P. Mysore, and M.L. Littman, “Activity Recognition from Accelerometer Data,” Proc. Seventh Innovative Applications of Artificial Intelligence Conf., pp. 1541-1546, 2005.
[35] E.O. Brigham and R.E. Morrow, “The Fast Fourier Transform,” IEEE Spectrum, vol. 4, no. 12, pp. 63-70, Dec. 1967.
[36] J.A. Ward, P. Lukowicz, and H.W. Gellersen, “Performance Metrics for Activity Recognition,” ACM Trans. Intelligent Systems and Technology, vol. 2, pp. 6:1-6:23, Jan. 2011.
[37] L. Ni, Y. Liu, Y. Lau, and A. Patil, “LANDMARC: Indoor Location Sensing Using Active RFID,” Proc. First IEEE Int'l Conf. Pervasive Computing and Comm., pp. 407-416, Mar. 2003.
8 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool