Enhanced Conformal Predictors for Indoor Localisation Based on Fingerprinting Method

  • Khuong An Nguyen
  • Zhiyuan Luo
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 412)

Abstract

We proposed the first Conformal Prediction (CP) algorithm for indoor localisation with a classification approach. The algorithm can provide a region of predicted locations, and a reliability measurement for each prediction. However, one of the shortcomings of the former approach was the individual treatment of each dimension. In reality, the training database usually contains multiple signal readings at each location, which can be used to improve the prediction accuracy. In this paper, we enhance our former CP with the Kullback-Leibler divergence, and propose two new classification CPs. The empirical studies show that our new CPs performed slightly better than the previous CP when the resolution and density of the training database are high. However, the new CPs performs much better than the old CP when the resolution and density are low.

Keywords

indoor localisation fingerprinting conformal prediction 

References

  1. 1.
    Chen, Y., Lymberopoulos, D., Liu, J., Priyantha, B.: Fm-based indoor localization. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, pp. 169–182. ACM (2012)Google Scholar
  2. 2.
    Nguyen, K., Luo, Z.: Conformal prediction for indoor localisation with fingerprinting method. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Karatzas, K., Sioutas, S. (eds.) AIAI 2012 Workshops, Part II. IFIP AICT, vol. 382, pp. 214–223. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Nguyen, K., Luo, Z.: Evaluation of bluetooth properties for indoor localisation. In: Progress in Location-Based Services, pp. 127–149. Springer (2013)Google Scholar
  4. 4.
    Nguyen, K.A.: Robot-based evaluation of bluetooth fingerprinting. Master’s thesis, Computer Lab, University of Cambridge (2011)Google Scholar
  5. 5.
    Shafer, G., Vovk, V.: A tutorial on conformal prediction. The Journal of Machine Learning Research 9, 371–421 (2008)MathSciNetMATHGoogle Scholar
  6. 6.
    Vovk, V., Gammerman, A., Shafer, G.: Algorithmic learning in a random world. Springer (2005)Google Scholar
  7. 7.
    Wang, H., Sen, S., Elgohary, A., Farid, M., Youssef, M., Choudhury, R.R.: No need to war-drive: Unsupervised indoor localization. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, pp. 197–210. ACM (2012)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Khuong An Nguyen
    • 1
  • Zhiyuan Luo
    • 1
  1. 1.Dept. of Computer ScienceRoyal Holloway, University of LondonEghamUnited Kingdom

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