Advertisement

Applying Classifiers in Indoor Location System

  • Gabriel VillarubiaEmail author
  • Francisco Rubio
  • Juan F. De Paz
  • Javier Bajo
  • Carolina Zato
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 221)

Abstract

Research in indoor location has acquired a growing importance during the recent years. The main objective is to obtain functional systems able of providing the most precise location, identification and guidance in real time. Currently, none of the existing indoor solutions have obtained location or navigation results as precise as the ones provided by the analog systems used outdoor, such as GPS. This paper presents an indoor location system based on Wi-Fi technology which, from the use of intensity maps and classifiers, allows effective and precise indoor location.

Keywords

indoor location system classifiers Wi-Fi 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bouckaert, R.R.: Bayesian Belief Networks: from Construction to Inference, Utrecht, Netherlands (1995)Google Scholar
  2. 2.
    Verma, T., Pearl, J.: An algorithm for deciding if a set of observed independencies has a causal explanation. In: Proc. of the Eighth Conference on Uncertainty in Artificial Intelligence, pp. 323–330 (1992)Google Scholar
  3. 3.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian Network Classifiers. Machine Learning 29, 131–163 (1997)CrossRefzbMATHGoogle Scholar
  4. 4.
    Glassner, A.: Principles of digital image synthesis. Morgan Kaufmann (1995)Google Scholar
  5. 5.
    Georgé, J.P., Gleizes, M.P., Glize, P.: Emergence of organisations, emergence of functions. In: Symposium on Adaptive Agents and Multi-Agent Systems, pp. 103–108 (2003)Google Scholar
  6. 6.
    De Paz, J.F., Rodríguez, S., Bajo, J., Corchado, J.M.: Multi-agent system for security control on industrial environments. International Transactions on System Science and Applications Journal 4(3), 222–226 (2008)Google Scholar
  7. 7.
    Chen, Y.-C., Chiang, J.-R., Chu, H.-H., Huang, P., Tsuid, A.W.: Sensor-Assisted Wi-Fi Indoor Location System for Adapting to Environmental Dynamics (2011)Google Scholar
  8. 8.
    Razavi, R.S., Perrot, J.-F., Guelfi, N.: Adaptive modeling: An approach and a method for implementing adaptive agents. In: Ishida, T., Gasser, L., Nakashima, H. (eds.) MMAS 2005. LNCS (LNAI), vol. 3446, pp. 136–148. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Giunchiglia, F., Mylopoulos, J., Perini, A.: The tropos software development methodology: Processes, models and diagrams. In: AAMAS 2002 Workshop on Agent Oriented Software Engineering (AOSE 2002), pp. 63–74 (2002)Google Scholar
  10. 10.
    Tapia, D.I., De Paz, J.F., Rodríguez, S., Bajo, J., Corchado, J.M.: Multi-Agent System for Security Control on Industrial Environments. International Transactions on System Science and Applications Journal 4(3), 222–226 (2008)Google Scholar
  11. 11.
    Duda, R.O., Hart, P.: Pattern classification and Scene Analysis. John Wisley & Sons, New York (1973)zbMATHGoogle Scholar
  12. 12.
    John, C.: Platt Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods, pp. 185–208 (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Gabriel Villarubia
    • 1
    Email author
  • Francisco Rubio
    • 1
  • Juan F. De Paz
    • 1
  • Javier Bajo
    • 2
  • Carolina Zato
    • 1
  1. 1.Department of Computer Science and AutomationUniversity of SalamancaSalamancaSpain
  2. 2.Departamento de Inteligencia Artificial, Facultad de InformáticaUniversidad Politécnica de MadridMadridSpain

Personalised recommendations