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Domain-constrained semi-supervised mining of tracking models in sensor networks

Published: 12 August 2007 Publication History

Abstract

Accurate localization of mobile objects is a major research problem in sensor networks and an important data mining application. Specifically, the localization problem is to determine the location of a client device accurately given the radio signal strength values received at the client device from multiple beacon sensors or access points. Conventional data mining and machine learning methods can be applied to solve this problem. However, all of them require large amounts of labeled training data, which can be quite expensive. In this paper, we propose a probabilistic semi supervised learning approach to reduce the calibration effort and increase the tracking accuracy. Our method is based on semi-supervised conditional random fields which can enhance the learned model from a small set of training data with abundant unlabeled data effectively. To make our method more efficient, we exploit a Generalized EM algorithm coupled with domain constraints. We validate our method through extensive experiments in a real sensor network using Crossbow MICA2 sensors. The results demonstrate the advantages of methods compared to other state-of-the-art object-tracking algorithms.

References

[1]
H. L. Chieu, S. W. Lee, and P. K. Leslie. Activity recognition from physiological data using conditional random fields, January 2006.
[2]
A. P. Dempster, N. M. Laird, and D. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, 1(39):1--38, 1977.
[3]
J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. 18th International Conf. on Machine Learning, pages 282--289. Morgan Kaufmann, San Francisco, CA, 2001.
[4]
J. Lester, T. Choudhury, N. Kern, G. Borriello, and B. Hannaford. A hybrid discriminative/generative approach for modeling human activities. In IJCAI, pages 766--772, 2005.
[5]
A. McCallum. Efficiently inducing features of conditional random fields. In C. Meek and U. Kjærulff, editors, UAI, pages 403--410. Morgan Kaufmann, 2003.
[6]
C. Neukirchen, D. Willett, and G. Rigoll. Soft State-Tying for HMM-Based Speech Recognition. In 5th International Conference on Spoken Language Processsing (ICSLP), pages 2999--3002, Sydney, 1998.
[7]
X. Nguyen, M. I. Jordan, and B. Sinopoli. A kernel-based learning approach to ad hoc sensor network localization. ACM Transactions on Sensor Networks, 1(1):134--152, 2005.
[8]
L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257--286, 1989.
[9]
A. Savvides, C. Han, and M. B. Strivastava. Dynamic fine-grained localization in ad-hoc networks of sensors. In Proceedings of the 7th Annual International Conference on Mobile Computing and Networking, pages 166--179, Rome, Italy, 2001.
[10]
C. Sutton and A. McCallum. An introduction to conditional random fields for relational learning. In L. Getoor and B. Taskar, editors, Introduction to Statistical Relational Learning. MIT Press, 2006.
[11]
J. Yin, X. Chai, and Q. Yang. High-level goal recognition in a wireless LAN. In Proceedings of the Nineteenth National Conference on Artificial Intelligence, pages 578--584, San Jose, CA, USA, July 2004.

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  1. Domain-constrained semi-supervised mining of tracking models in sensor networks

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      cover image ACM Conferences
      KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
      August 2007
      1080 pages
      ISBN:9781595936097
      DOI:10.1145/1281192
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 12 August 2007

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      Author Tags

      1. CRF
      2. EM
      3. calibration
      4. localization
      5. sensor networks
      6. tracking

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      KDD '07 Paper Acceptance Rate 111 of 573 submissions, 19%;
      Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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      • (2013)Where Will You Go? Mobile Data Mining for Next Place PredictionProceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery - Volume 805710.1007/978-3-642-40131-2_13(146-158)Online publication date: 26-Aug-2013
      • (2012)Clueless nodes to network-cognizant smart nodes: Achieving network awareness in wireless sensor networks2012 IEEE Consumer Communications and Networking Conference (CCNC)10.1109/CCNC.2012.6181081(174-179)Online publication date: Jan-2012
      • (2011)H-cluster: A Novel Efficient Algorithm for Data Clustering in Sensor NetworksJournal of Communications10.4304/jcm.6.2.168-1786:2Online publication date: 1-Apr-2011
      • (2009)Sensor network localization using kernel spectral regressionWireless Communications and Mobile Computing10.1002/wcm.820(n/a-n/a)Online publication date: 2009

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