A Hybrid Approach for Indoor Positioning

Öz

Positioning systems have wide range of applications with the developing technology. Global Positioning System (GPS) is an efficient solution for outdoor applications but it gives poor accuracy in indoor environment. And, various methods are proposed in the literature such as geometric-based, fingerprint-based, etc. In this study, a hybrid approach that uses both clustering and classification is developed for fingerprint-based method. Information gain based feature selection method is used for selection of the most appropriate features from the WiFi fingerprint dataset in the initial step of this approach. Then, Expectation Maximization (EM) algorithm is applied for clustering purpose. Then, decision tree algorithm is used as a classification task for each cluster. Experimental results indicate that applied algorithms lead to a substantial improvement on localization accuracy. Since, cluster specific decision tree models reduce the size of the tree significantly; computational time of position phase is also reduced.

Anahtar Kelimeler

• Fingerprinting,indoor positioning,access point selection,clustering,classification,feature selection,expectation maximization,decision tree,received signal strength

Kaynakça

1. G. M. Djuknic, and R. E. Richton, “Geolocation and Assisted GPS,” IEEE Computer, vol. 2, pp. 123–125, Feb. 2001.
2. P. Bahl and V. N. Padmanabhan, “RADAR: An InBuilding RF-based User Location and Tracking System,” in Proc. IEEE INFOCOM, 2000, pp. 775–784.
3. A. Abusara and M. Hassan, “Enhanced fingerprinting in wlan-based indoor positioning using hybrid search techniques,” in International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2015, pp. 1–6, Feb. 2015.
4. H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning techniques and systems,” Systems, Man, and Cybernetics, Part C: IEEE Transactions on Applications and Reviews, vol. 37, pp. 1067–1080, Nov. 2007.
5. S. Bozkurt Keser, U. Yayan, A. Yazici, S. Gunal, "A priori verification and validation study of RFKON database", International Journal of Computer Science: Theory and Application, vol. 5, 20-27, 2016.
6. D. Li, B. Zhang, Z. Yao and C. Li, "A feature scaling based k-nearest neighbor algorithm for indoor positioning system," 2014 IEEE Global Communications Conference, Austin, TX, 2014, pp. 436-441.
7. Y. Ha, E. Ae-cheoun, and B. Yung-cheol, "Efficient sensor localization for indoor environments using classification of link quality patterns", International Journal of Distributed Sensor Networks, 2013.
8. S. Eisa, J. Peixoto, F. Meneses, and A. Moreira, "Removing useless APs and fingerprints from WiFi indoor positioning radio maps", International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp.1-7, Oct. 2013.

9. V. Seshadri, V. G. Zaruba, and M. Huber, "A Bayesian sampling approach to in-door localization of wireless devices using received signal strength indication", Third IEEE International Conference on Pervasive Computing and Communications (PERCOM 2005), pp.75-84, March 2005.
10. X. Chai, and Q. Yang, "Reducing the calibration effort for location estimation using unlabeled samples", Third IEEE International Conference on Pervasive Computing and Communications (PERCOM 2005), pp. 95-104, March 2005.
11. T. Liu, P. Bahl, and I. Chlamtac, “A hierarchical position-prediction algorithm for efficient management of resources in cellular networks”, Global Telecommunication Conference (GLOBECOM ’97), IEEE, vol. 2, pp. 982-986, Nov. 1997.
12. M. Isard, and A. Blake, “Contour tracking by stochastic propagation of conditional density”, Computer Vision (ECCV ’96), pp. 343-356, 1996.
13. M. A. Youssef, A. Agrawala, A. U. Shankar, and S. H. Noh, “A probabilistic clustering-based indoor location determination system”, Tech. Report, University of Maryland at College Park, CS-TR 4350, March 2002.
14. J. Ledlie, “Method and apparatus for on-device positioning using compressed fingerprint archives,” June 2011.
15. E. Laitinen, E. Lohan, J. Talvitie, and S. Shrestha, “Access point significance measures in WLAN-based location,” in 2012 9th Workshop on Positioning Navigation and Communication (WPNC), pp. 24–29, Mar. 2012.
16. A. Abusara, M. S. Hassan, and M. H. Ismail, "RSS fingerprints dimensionality reduction in WLAN-based indoor positioning", IEEE 2016 Wireless Telecommunications Symposium (WTS), April 2016.
17. A. G.Karegowda, A. S. Manjunath, and M. A. Jayaram, “ Comparative study of attribute selection using gain ratio and correlation based feature selection”, International Journal of Information Technology and Knowledge Management, vol. 2,pp. 271-277, Dec. 2010.
18. I. H. Witten, E Frank, MA Hall. Data Mining: Practical Machine Learning Tools and Techniques: Practical Machine Learning Tools and Techniques. Elsevier, 2011.
19. L. Rokach, O. Maimon, Decision Trees. In The Data Mining and Knowledge Discovery Handbook. Springer, pp. 165–192, 2005.