Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Location aware computing is popularized and location information use has important due to huge application of mobile computing devices and local area wireless networks. In this paper, we have proposed a method based on Semi-supervised Locally Linear Embedding for indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so, the RSS vector received by the mobile station has large dimensions (i.e. 101). At first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user’s location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the nonline-of-sight (NLOS) situation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahmad U., Gavrilov A., Lee Y., Lee S. (2008) Context-aware, self-scaling Fuzzy ArtMap for received signal strength based location systems. Soft Computing-A Fusion of Foundations, Methodologies and Applications 12(7): 699–713

    Google Scholar 

  2. Bahl, P., & Padmanabhan, V. (2000). RADAR: An in-building RF-based user location and tracking system. In Proceedings of 19th IEEE annual joint conference of computer and communications societies, INFOCOM 2000 (Vol. 2, pp. 775–784).

  3. Bahl, P., Padmanabhan, V., & Balachandran, A. (2000). Enhancements to the RADAR user location and tracking system. Microsoft Research (MSR-TR-2000-12), 13.

  4. Battiti, R., Nhat, T., & Villani, A. (2002). Location-aware computing: A neural network model for determining location in wireless LANs. Technical Report DIT-02-083, Ingegneria e Scienza dell’Informazione. University of Trento.

  5. Burrell, G., & Kao, D. M. About GPS. http://www.garmin.com/aboutGPS/. Accessed 20 April 2011.

  6. Chen, C. (2005). Hybrid location estimation and tracking system for mobile devices. In Proceedings of 61st IEEE vehicular technology conference, VTC 2005 (Vol. 4, pp. 2648–2652).

  7. Ding, X., Li, H., Li, F., & Wu, J. (2008). A novel infrastructure WLAN locating method based on neural network. In Proceedings of 4th Asian conference on internet engineering (pp. 47–55).

  8. Enge, P., & Misra, P. (1999). Scanning the special issue/technology on the global positioning system. In Proceedings of IEEE, special issue on GPS (Vol. 87, pp. 3–15).

  9. Gupta, A., Tapaswi, S., & Jain, V. (2009). Recurrent grid based voting approach for location estimation in wireless sensor networks. In Proceedings of symposia and workshops on ubiquitous, autonomic and trusted computing, UIC-ATC’09 (pp. 263–267).

  10. Hightower J., Borriello G. (2001) Location systems for ubiquitous computing. Computer 34(8): 57–66

    Article  Google Scholar 

  11. Kashima, H., Suzuki, S., Hido, S., Tsuboi, Y., Takahashi, T., Ide, T. et al. (2007). A semi-supervised approach to indoor location estimation. http://www.geocities.co.jp/Technopolis/5893/publication/ICDMDMC2007-1.pdf. Accessed 20 April 2011.

  12. Krishnan, P., Krishnakumar, A., Ju, W. H., Mallows, C., & Gamt, S. (2004). A system for lease: Location estimation assisted by stationary emitters for indoor rf wireless networks. In Proceedings of 23rd IEEE annual joint conference of computer and communications societies, INFOCOM 2004 (Vol. 2, pp. 1001–1011). doi:10.1109/INFCOM.2004.1356987.

  13. Liu H., Darabi H., Banerjee P., Liu J. (2007) Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37(6): 1067–1080

    Article  Google Scholar 

  14. Pahlavan K., Li X., Makela J. (2002) Indoor geolocation science and technology. IEEE Communications Magazine 40(2): 112–118. doi:10.1109/35.983917

    Article  Google Scholar 

  15. Roweis S. T., Saul L. K. (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500): 2323–2326. doi:10.1126/science.290.5500.2323

    Article  Google Scholar 

  16. Saul L. K., Roweis S. T. (2003) Think globally, fit locally: Unsupervised learning of low dimensional manifolds. The Journal of Machine Learning Research 4: 119–155

    MathSciNet  Google Scholar 

  17. Yang, Q., Pan, S. J., & Zang, V. W. (2007). IEEE ICDM data mining contest 2007. http://www.cse.ust.hk/~qyang/ICDMDMC07/. Accessed 20 April 2011.

  18. Yang Q., Pan S., Zheng V. (2008) Estimating location using wi-fi. IEEE Intelligent Systems 23(1): 8–13

    Article  Google Scholar 

  19. Yang B., Xu J., Yang J., Li M. (2010) Localization algorithm in wireless sensor networks based on semi-supervised manifold learning and its application. Cluster Computing 13(4): 435–446

    Article  Google Scholar 

  20. Yin J., Hu D., Zhou Z. (2007) Growing locally linear embedding for manifold learning. Journal of Pattern Recognition Research 2(1): 1–16

    Google Scholar 

  21. Youssef, M., & Agrawala, A. (2004). Handling samples correlation in the horus system. In Proceedings of 23rd IEEE annual joint conference of computer and communications societies, INFOCOM 2004 (pp. 1023–1031).

  22. Youssef M., Agrawala A. (2008) The Horus location determination system. Wireless Networks 14(3): 357–374

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Atal Bihari Vajpayee – Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, India

    V. K. Jain, Shashikala Tapaswi & Anupam Shukla

Authors
  1. V. K. Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shashikala Tapaswi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anupam Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. K. Jain.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jain, V.K., Tapaswi, S. & Shukla, A. Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks. Wireless Pers Commun 67, 879–893 (2012). https://doi.org/10.1007/s11277-011-0416-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-011-0416-2

Keywords

Search

Navigation