Advertisement

Soft Computing

, Volume 17, Issue 10, pp 1817–1831 | Cite as

A multiclassifier approach for topology-based WiFi indoor localization

  • Krzysztof Trawiński
  • Jose M. AlonsoEmail author
  • Noelia Hernández
Methodologies and Application

Abstract

People localization is required for many novel applications like for instance proactive care for the elders or people suffering degenerative dementia such as Alzheimer’s disease. This paper introduces a new system for people localization in indoor environments. It is based on a topology-based WiFi signal strength fingerprint approach. Accordingly, it is a robust, cheap, ubiquitous and non-intrusive system which does require neither the installation of extra hardware nor prior knowledge about the structure of the environment under consideration. The well-known curse of dimensionality critically emerges when dealing with complex environments. The localization task turns into a high dimensional classification task. Therefore, the core of the proposed framework is a fuzzy rule-based multiclassification system, using standard methodologies for the component classifier generation such as bagging and random subspace, along with fuzzy logic to deal with the huge uncertainty that is characteristic of WiFi signals. Achieved results in two real environments are encouraging, since they clearly overcome those ones provided by the well-known nearest neighbor fingerprint matching algorithm, which is usually considered as a baseline for WiFi localization.

Keywords

WiFi localization Classifier ensembles Bagging Random subspace Fuzzy rule-based multiclassification systems 

Notes

Acknowledgments

This work has been partly supported by the Spanish Ministry of Economy and Competitiveness under INFANTREE project (JCI-2011-09839), ABSYNTHE project (TIN2011-29824-C02-01 and TIN2011-29824-C02-02), and the European Centre for Soft Computing (ECSC) located at Mieres (Asturias, Spain).

References

  1. Alonso JM, Ocaña M, Hernández N, Herranz F, Llamazares A, Sotelo MA, Bergasa LM, Magdalena L (2011) Enhanced WiFi localization system based on soft computing techniques to deal with small-scale variations in wireless sensors. Appl Soft Comput 11(8):4677–4691CrossRefGoogle Scholar
  2. Alonso JM, Ocaña M, Sotelo MA, Bergasa LM, Magdalena L (2009) WiFi localization system using fuzzy rule-based classification. Lecture Notes in Computer Science, Comput Aid Syst Theory-EUROCAST09 5717:383–390CrossRefGoogle Scholar
  3. Alvarez-Alvarez A, Alonso JM, Trivino G, Hernández N, Herranz F, Llamazares A, Ocaña M (2010) Human activity recognition applying computational intelligence techniques for fusing information related to WiFi positioning and body posture. In: Proceedings of the IEEE international conference on Fuzzy systems, pp 1881–1885Google Scholar
  4. Astrain JJ, Villadangos J, Garitagoitia JR, González de Mendívil JR, Cholvi V (2006) Fuzzy location and tracking on wireless networks. In: Proceedings of the 4th ACM international workshop on mobility management and wireless access, pp 84–91Google Scholar
  5. Bahillo A, Lorenzo RM, Mazuelas S, Fernandez P, Abril EJ (2009) Assessment of the shadow caused by the human body on the personal RF dosimeters reading in multipath environments. In: Biomedical Engineering, pp 133–144Google Scholar
  6. Bahl P, Padmanabhan V (2000) RADAR: an in-building RF-based user location and tracking system. In: Proceedings of the IEEE computer and communications societies, pp 775–784Google Scholar
  7. Banfield RE, Hall LO, Bowyer KW, Kegelmeyer WP (2007) A comparison of decision tree ensemble creation techniques. IEEE Trans Pattern Anal Mach Intell 29(1):173–180CrossRefGoogle Scholar
  8. Bonissone PP, Cadenas JM, Garrido MC, Díaz-Valladares RA (2010) A fuzzy random forest. Int J Approx Reason 51(7):729–747CrossRefGoogle Scholar
  9. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140MathSciNetzbMATHGoogle Scholar
  10. Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefzbMATHGoogle Scholar
  11. Chiang KW, Huang YW (2008) An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications. Appl Soft Comput 8(1):722–733MathSciNetCrossRefGoogle Scholar
  12. Cohen WW (1995) Fast effective rule induction. In: Proceedings of the twelfth international conference on machine learning, Morgan Kaufmann, pp 115–123Google Scholar
  13. Cordón O, del Jesus MJ, Herrera F (1999) A proposal on reasoning methods in fuzzy rule-based classification systems. Int J Approx Reason 20:21–45Google Scholar
  14. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21–27CrossRefzbMATHGoogle Scholar
  15. Dharne AG, Lee J, Jayasuriya S (2006) Using fuzzy logic for localization in mobile sensor networks: simulations and experiments. In: Proceedings of the American control conference. IEEE, pp 2066–2071Google Scholar
  16. Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40(2):139–157CrossRefGoogle Scholar
  17. Elnahrawy E, Li X, Martin RP (2004) The limits of localization using signal strength: a comparative study. In: First annual IEEE communications society conference on sensor Ad Hoc communications and networks, pp 406–414Google Scholar
  18. Enge P, Misra P (1999) Special issue on GPS: the global positioning system. In: Proceedings of the IEEE, pp 3–172Google Scholar
  19. Gallagher TJ, Li B, Dempster AG, Rizos C (2010) A sector-based campus-wide indoor positioning system. In: IEEE international conference on indoor positioning and indoor navigation, pp 1–8Google Scholar
  20. Garcia-Valverde T, Garcia-Sola A, Gomez-Skarmeta A, Botia JA, Hagras H, Dooley J, Callaghan V (2012) An adaptive learning fuzzy logic system for indoor localisation using Wi-Fi in ambient intelligent environments. In: Proceedings of the IEEE World congress on computational intelligence, pp 25–32Google Scholar
  21. Hernández N, Alonso JM, Magro M, Ocaña M (2012) Hierarchical WiFi localization system. In: international workshop on perception in robotics, IEEE intelligent vehicles symposium, pp P21.1–P21.6Google Scholar
  22. Herrero-Pereza D, Martinez-Barbera H, LeBlanc K, Saffiotti A (2010) Fuzzy uncertainty modeling for grid based localization of mobile robots. Int J Approx Reason 51:912–932CrossRefGoogle Scholar
  23. Ho T (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844CrossRefGoogle Scholar
  24. Hühn JC, Hüllermeier E (2009) FURIA: an algorithm for unordered fuzzy rule induction. Data Min Knowl Disc 19(3):293–319CrossRefGoogle Scholar
  25. Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20:226–239CrossRefGoogle Scholar
  26. Kuncheva LI (2001) Using measures of similarity and inclusion for multiple classifier fusion by decision templates. Fuzzy Sets Syst 122:401–407MathSciNetCrossRefzbMATHGoogle Scholar
  27. Kuncheva LI (2002) A theoretical study on six classifier fusion strategies. IEEE Trans Pattern Anal Mach Intell 24:281–286CrossRefGoogle Scholar
  28. Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, New JerseyGoogle Scholar
  29. Kuncheva LI, Bezdek JC, Duin RPW (2001) Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recogn Lett 34:299–314CrossRefzbMATHGoogle Scholar
  30. Magdalena L (2008) What is soft computing? revisiting possible answers. In: 8th International FLINS conference on computational intelligence in decision and control, World Scientific, Singapore, pp 3–10Google Scholar
  31. Menendez P, Campomanes C, Trawiński K, Alonso JM (2011) Topology-based indoor localization by means of WiFi fingerprinting with a computational intelligent classifier. In: Proceedings of the 11th IEEE international conference on intelligent system design and applications, pp 1020–1025Google Scholar
  32. Nerguizian C, Belkhous S, Azzouz A, Nerguizian V, Saad M (2004) Mobile robot geolocation with received signal strength (RSS) fingerprinting technique and neural networks. In: IEEE international conference on industrial technology, pp 1183–1185Google Scholar
  33. Optiz D, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169–198Google Scholar
  34. Outemzabet S, Nerguizian C (2008) Accuracy enhancement of an indoor ANN-based fingerprinting location system using Kalman filtering. In: 19th IEEE international symposium on personal, indoor and mobile radio communications, pp 1–5Google Scholar
  35. Outemzabet S, Nerguizian C (2008) Accuracy enhancement of an indoor ANN-based fingerprinting location system using particle filtering and a low-cost sensor. In: IEEE international conference on vehicular technology, pp 2750–2754Google Scholar
  36. Palmer N, Kemp R, Kielmann T, Bal H (2012) The case for smartphones as an urgent computing client platform. Procedia Comput Sci 9:1667–1676CrossRefGoogle Scholar
  37. Panov P, Džeroski S (2007) Combining bagging and random subspaces to create better ensembles. In: Proceedings of the 7th international conference on intelligent data analysis. Springer, Berlin, pp 118–129Google Scholar
  38. Paul R, Aguirre E, Garcia-Silvente M, Muñoz-Salinas R (2012) A new fuzzy based algorithm for solving stereo vagueness in detecting and tracking people. Int J Approx Reason 53(4):693–708CrossRefGoogle Scholar
  39. Quinlan JR (1991) Improved estimates for the accuracy of small disjuncts. Mach Learn 6(1):93–98Google Scholar
  40. Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619–1630CrossRefGoogle Scholar
  41. Schapire R (1990) The strength of weak learnability. Mach Learn 5(2):197–227Google Scholar
  42. Trawiński K, Cordón O, Quirin A (2011) On designing fuzzy rule-based multiclassification systems by combining FURIA with bagging and feature selection. Int J Uncertain Fuzziness Knowl Based Syst 19(4):589–633CrossRefGoogle Scholar
  43. Tsymbal A, Pechenizkiy M, Cunningham P (2005) Diversity in search strategies for ensemble feature selection. Inform Fusion 6(1):83–98CrossRefGoogle Scholar
  44. Webb GI (1999) Decision tree grafting from the all-tests-but-one partition. In: Sixteenth international joint conference on artificial intelligence. Morgan Kaufmann, pp 702–707Google Scholar
  45. Witten IH, Frank E, Hall MA (2011) Data Mining: practical machine learning tools and techniques. 3rd edn, Morgan Kaufmann, San FranciscoGoogle Scholar
  46. Woods K, Kegelmeyer WP, Bowyer K (1997) Combination of multiple classifiers using local accuracy estimates. IEEE Trans Pattern Anal Mach Intell 19:405–410CrossRefGoogle Scholar
  47. Youssef M, Agrawala A (2003) Small-scale compensation for wlan location determination systems. In: Proceedings of the ACM workshop on wireless security, pp 11–20Google Scholar
  48. Yun S, Lee J, Chung W, Kim E, Kim S (2009) A soft computing approach to localization in wireless sensor networks. Expert Syst Appl 36(4):7552–7561CrossRefGoogle Scholar
  49. Zhou ZH (2005) Ensembling local learners through multimodal perturbation. IEEE Trans Syst Man Cybern Part B Cybern 35(4):725–735CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Krzysztof Trawiński
    • 1
  • Jose M. Alonso
    • 1
    Email author
  • Noelia Hernández
    • 2
  1. 1.European Centre for Soft ComputingMieresSpain
  2. 2.RobeSafe Research Group, Department of ElectronicsUniversity of AlcaláAlcalá de Henares, MadridSpain

Personalised recommendations