Advertisement

Fingerprint-Based Support Vector Machine for Indoor Positioning System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 670)

Abstract

The position of a movable object is required in an indoor environment for providing various business interest services and for emergency services. The techniques implemented on WLAN (802.11b Wireless LANs) endow with more ubiquitous (Feng et al. in IEEE Trans Mob Comput 12(12), 2012, [1]) within the environment and the requirement for additional hardware is not necessary, thereby reducing infrastructure cost and enhancing the value of wireless data network. The received signal strength (RSS) from various reference points (RP) were recorded by a tool and fingerprint radio map is constructed. The signal property of a fingerprint will differ in each point. The location can be found by comparing the current signal strength with already collected radio maps. Almost all indoor environments are equipped with Wi-Fi devices. No additional hardware is required for the setup. In this paper, we introduce SVM classifier (Roos et al. in IEEE Trans Mob Comput 1(1), 59–69, 2002 [2]) as a methodology with minimum cost and without scarifying accuracy. The obtained results show minimal location error and accurate location of the object.

Keywords

Pervasive computing Received signal strength Indoor positioning Support vector machine 

References

  1. 1.
    Chen Feng, Wain Sy Anthea Au, Shahrokh Valaee, Zhenhui Tan.: “Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing”, IEEE Transactions on mobile computing. 2012, vol. 11, no. 12.Google Scholar
  2. 2.
    Roos, T., Myllymaki, P., and Tirri, H. “A Statistical Modelling Approach to Location Estimation”, IEEE Transactions on Mobile Computing 1, 1 (January–March 2002), 59–69Google Scholar
  3. 3.
    R. Bruno and F. Delmastro, “Design and analysis of a bluetooth-based indoor localization system,” in Proceedings of IEEE International Conference on Personal Wireless Communications, vol. 27, pp. 711–725, Venive, Italy, September 2003.Google Scholar
  4. 4.
    H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol. 37, pp. 1067–1080, November 2007.Google Scholar
  5. 5.
    Paramvir Bahl and Venkata N. Padmanabhan, “RADAR: an in-building RF-based user location and tracking system,” in INFOCOM 2000 Proceedings of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, March 2000, vol. 2, pp. 775–784Google Scholar
  6. 6.
    T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, and J. Sievanen, “A Probabilistic Approach to WLAN User Location Estimation,” Int’l J. Wireless Information Networks, vol. 9, no. 3, pp. 155–164, July 2002.Google Scholar
  7. 7.
    A. Kushki, K.N. Plataniotis, and A.N. Venetsanopoulos, “Kernel-Based Positioning in Wireless Local Area Networks,” IEEE Trans. Mobile Computing, vol. 6, no. 6, pp. 689–705, June 2007Google Scholar
  8. 8.
    M. Youssef and A. Agrawala, “The Horus WLAN Location Determination System,” Proceedings Third International Conference of Mobile Systems, Applications, and Services, pp. 205–218, 2005.Google Scholar
  9. 9.
    Gwon, Y., Jain, R., and Kawahara, T. “Robust Indoor Location Estimation of Stationary and Mobile Users”, In IEEE Infocom (March 2004).Google Scholar
  10. 10.
    C. Feng, W.S.A. Au, S. Valaee, and Z. Tan.: “Compressive Sensing Based Positioning Using RSS of WLAN Access Points”, pp. 1–9, Proc. IEEE INFOCOM, 2010.Google Scholar
  11. 11.
    Haojun Huang. Jianguo Zhou. Wei Li. Juanbao Zhang. Xu Zhang. Guolin Hou: “Wearable indoor Localisation approach in Internet of Things”, IET Networks, (Jul. 2016), pp. 1–5.Google Scholar
  12. 12.
    D. A. Tran and T. Nguyen, “Localization in wireless sensor networks based on support vector machines Parallel and Distributed Systems”, IEEE Transactions on, vol. 19, no. 7, (2008), pp. 981–994.Google Scholar
  13. 13.
    L. Pei, J. Liu, R. Guinness, Y. Chen, et al., “Using LS-SVM based Motion recognition for smart phone indoor wireless positioning”, Sensors, vol. 12, no. 5, (2012), pp. 6155–6175.Google Scholar
  14. 14.
    T. Graepel, “Kernel Matrix Completion by Semi-Definite Programming” Proc. Int’l Conf. Artificial Neural Networks, 2002.Google Scholar
  15. 15.
    G.Kousalya, P Narayanasamy, Jong Hyuk Park, Tai-hoon Kim.: “Predictive handoff mechanism with real-time mobility tracking in a campus wide wireless network considering”, ITS Computer Communications, 2008, vol. 31, no. 12, pp. 2781–2789Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Sri Krishna College of TechnologyCoimbatoreIndia
  2. 2.Coimbatore Institute of TechnologyCoimbatoreIndia

Personalised recommendations