Cluster Computing

, Volume 13, Issue 4, pp 435–446 | Cite as

Localization algorithm in wireless sensor networks based on semi-supervised manifold learning and its application

  • Bin YangEmail author
  • Jinwu Xu
  • Jianhong Yang
  • Min Li


Localization of mobile nodes in wireless sensor network gets more and more important, because many applications need to locate the source of incoming measurements as precise as possible. Many previous approaches to the location-estimation problem need know the theories and experiential signal propagation model and collect a large number of labeled samples. So, these approaches are coarse localization because of the inaccurate model, and to obtain such data requires great effort. In this paper, a semi-supervised manifold learning is used to estimate the locations of mobile nodes in a wireless sensor network. The algorithm is used to compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use independent development nodes to setup the network in metallurgical industry environment, outdoor and indoor. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared with RADAR localization systems.


Wireless sensor networks Mobile nodes Localization Semi-supervised manifold learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Akyildiz, I.F., Weilian, S., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. Commun. Mag. 40(8), 102–114 (2002) CrossRefGoogle Scholar
  2. 2.
    Savarese, C., Rabaey, J.M., Reutel, J.: Localization in distributed Ad-hoc wireless sensor networks. In: Proc ICASSP, Salt Lake City UT, pp. 2037–2040 (2001) Google Scholar
  3. 3.
    Patwari, N., Hero, A.O. III, Perkins, M. et al.: Relative location estimation in wireless sensor networks. IEEE Trans. Signal Process. 51(8), 2137–2148 (2003) CrossRefGoogle Scholar
  4. 4.
    Girod, L., Estrin, D.: Robust range estimation using acoustic and multimodal sensing. In: Proceedings in 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1312–1320 (2001) Google Scholar
  5. 5.
    Andreas, S., Chih-Chieh, H., Mani, S.: Dynamic fine-grained localization in Ad-Hoc networks of sensors. In: Proceedings of the 7th Annual International Conference on Mobile Computing and Networking, pp. 166–179 (2001) Google Scholar
  6. 6.
    Niculescu, D., Nath, B.: Ad hoc positioning system (APS) using AoA. In: IEEE InfoCom, pp. 1734–1743 (2003) Google Scholar
  7. 7.
    Patwari, N., Hero, A.O., III: Using proximity and quantized RSS for sensor localization in wireless networks. In: Proceedings of the 2nd ACM International Conference on Wireless Sensor Networks and Applications, pp. 20–29 (2003) Google Scholar
  8. 8.
    Bulusu, N., Heidemann, J., Estrin, D.: GPS-less low cost outdoor localization for very small devices. IEEE Pers. Commun. Mag. 7(5), 28–34 (2000) CrossRefGoogle Scholar
  9. 9.
    Tian, H., Chengdu, H., Brian, M., et al.: Range-free localization schemes for large scale sensor networks. In: MobiCom 2003 Google Scholar
  10. 10.
    Niculescu, D., Nath, B.: DV based positioning in Ad Hoc networks. J. Telecommun. Syst. 22, 267–280 (2003) CrossRefGoogle Scholar
  11. 11.
    Radhika, N., Howard, S., Jonathan, B.: Organizing a global coordinate system from local information on an Ad Hoc sensor network. In: International Workshop on Information Processing in Sensor Networks (IPSN), April 2003 Google Scholar
  12. 12.
    Liu, P.X., Zhang, X.M., Tian, S., Zhao, Z.W., Sun, P.: A novel virtual anchor node-based localization algorithm for wireless sensor networks. In: Proc. of the Sixth International Conference on Networking (ICN’07), April 2007 Google Scholar
  13. 13.
    Nguyen, X.L., Jordan, M., Sinopoli, B.: A kernel-based learning approach to ad hoc sensor network localization. ACM Trans. Sens. Netw. 1(1), 134–152 (2005) CrossRefGoogle Scholar
  14. 14.
    Junfeng, J., James, P., Kwoka, T., et al.: Multidimensional vector regression for accurate and low-cost location estimation in pervasive computing. IEEE Trans. Knowl. Data Eng. 18, 1181–1193 (2006) CrossRefGoogle Scholar
  15. 15.
    Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006) MathSciNetGoogle Scholar
  16. 16.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003) zbMATHCrossRefGoogle Scholar
  17. 17.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. Neural Inf. Process. Syst. 14, 585–591 (2002) (NIPS’2001) Google Scholar
  18. 18.
    Bahl, P., Padmanabhan, V.: RADAR: An in building RF-based user location and tracking system. In: Proceedings of the Conference on Computer Communications, pp. 775–784 (2000) Google Scholar
  19. 19.
    Sheng-Po, K., Bing-Jhen, W., Wen-Chih, P., Yu-Chee, T.: Cluster enhanced techniques for pattern matching localization systems. In: Proceedings of the 4th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS 2007), Pisa, Italy, 2007, pp. 8–11 Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.National Center of Material Service SafetyUniversity of Science and Technology BeijingBeijingChina
  2. 2.School of Mechanical EngineeringUniversity of Science and Technology BeijingBeijingChina

Personalised recommendations