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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Probabilistic Support Vector Machine Localization in Wireless Sensor Networks

  • Received : 2010.11.17
  • Accepted : 2011.05.20
  • Published : 2011.12.31
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Abstract

Sensor networks play an important role in making the dream of ubiquitous computing a reality. With a variety of applications, sensor networks have the potential to influence everyone's life in the near future. However, there are a number of issues in deployment and exploitation of these networks that must be dealt with for sensor network applications to realize such potential. Localization of the sensor nodes, which is the subject of this paper, is one of the basic problems that must be solved for sensor networks to be effectively used. This paper proposes a probabilistic support vector machine (SVM)-based method to gain a fairly accurate localization of sensor nodes. As opposed to many existing methods, our method assumes almost no extra equipment on the sensor nodes. Our experiments demonstrate that the probabilistic SVM method (PSVM) provides a significant improvement over existing localization methods, particularly in sparse networks and rough environments. In addition, a post processing step for PSVM, called attractive/repulsive potential field localization, is proposed, which provides even more improvement on the accuracy of the sensor node locations.

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References

  1. K. Roemer and F. Mattern, "The Design Space of Wireless Sensor Networks," IEEE Wireless Commun., vol. 11, no. 6, Dec. 2004, pp. 54-61. https://doi.org/10.1109/MWC.2004.1368897
  2. L.M. Pestana Leao de Brito and L.M. Rodríguez Peralta, "An Analysis of Localization Problems and Solutions in Wireless Sensor Networks," Polytechnical Studies Rev., vol. 6, no. 9, 2008.
  3. V. Ramadurai and M.L. Sichitiu, "Localization in Wireless Sensor Networks: A Probabilistic Approach," Proc. Int. Conf. Wireless Netw., Las Vegas, NV, June 2003, pp. 275-281.
  4. A.A. Kannan, G. Mao, and B. Vucetic, "Simulated Annealing Based Wireless Sensor Network Localization," J. Comput., vol. 1, no. 2, 2006, pp. 15-22.
  5. C. Cortes and V. Vapnik, "Support-Vector Networks," Mach. Learning, vol. 20, no. 3, Sept. 1995, pp. 273-297.
  6. D.A. Tran and T. Nguyen, "Localization in Wireless Sensor Networks Based on Support Vector Machines," IEEE Trans. Parallel Distributed Syst., vol. 19, no. 7, July 2008, pp. 981-994. https://doi.org/10.1109/TPDS.2007.70800
  7. D.A. Tran and T. Nguyen, "Support Vector Classification Strategies for Localization in Sensor Networks," Proc. IEEE Int. Conf. Commun. Electron., 2006.
  8. A. Madevska-Bogdanova, D. Nikolik, and L.M.G. Curfs, "Probabilistic SVM Outputs for Pattern Recognition Using Analytical Geometry," Neurocomput., vol. 62, Dec. 2004, pp. 293-303. https://doi.org/10.1016/j.neucom.2003.03.002
  9. J.C. Platt, "Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods," Advances in Large Margin Classifiers, A.J. Smola et al., Eds., MIT Press, 1999, pp. 61-74.
  10. H. Lin, C. Lin, and R.C. Weng, "A Note on Platt's Probabilistic Outputs for Support Vector Machines," Mach. Learning, vol. 68, no. 3, Oct. 2007, pp. 267-276. https://doi.org/10.1007/s10994-007-5018-6
  11. N.B. Priyantha et al., "Anchor-Free Distributed Localization in Sensor Networks," Proc. 1st Int. Conf. Embedded Netw. Sensor Syst. LA, CA, 2003, pp. 340-341.
  12. Y. Koren and J. Borenstein, "Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation," IEEE Int. Conf. Robot. Autom., vol. 2, Sacramento, CA, 1991, pp. 1398-1404.
  13. G. Luh and W. Liu, "Dynamic Mobile Robot Navigation Using Potential Field Based Immune Network,"10th World Multi-Conf. Syst., Cybern., Inf., vol. 2, July 2006, pp. 246-251.
  14. X. Nguyen, M.I. Jordan, and B. Sinopoli, "A Kernel-Based Learning Approach to Ad Hoc Sensor Network Localization," ACM Trans. Sensor Netw., vol. 1, no. 1, Aug. 2005, pp. 134-152. https://doi.org/10.1145/1077391.1077397
  15. D.A. Tran, X. Nguyen, and T. Nguyen, "Learning Techniques for Localization in Wireless Sensor Networks," Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking, 2008.

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