Hybrid classifier for Wi-Fi fingerprinting system

Author

Mundo, Lersan B del ; Macatangga, Raymond S.

Author_Institution

Coll. of Comput. Studies, Our Lady of Fatima Univ., Valenzuela, Philippines

fYear

2012

fDate

15-17 Oct. 2012

Firstpage

107

Lastpage

112

Abstract

Many algorithms proposed for indoor positioning using Wi-Fi based networks, those that rely on hybrid-based approach have been demonstrated to outperform those based on deterministic, probabilistic, and pattern recognition in terms of accuracy. We construct a hybrid algorithm architecture on three representative Wi-Fi fingerprinting techniques, based on the K-Nearest Neighbor (k-NN), Naive Bayes Classifier (NBC) and Support Vector Machines (SVM) algorithms. Our experiments show that hybrid-based approach outperformed all three K-NN, NBC, and SVM-based fingerprinting, achieving accuracies of 89.47% within 2 meters or better within our testbed.

Keywords

Bayes methods; indoor radio; pattern classification; support vector machines; wireless LAN; K-nearest neighbor; SVM algorithm; Wi-Fi based network; Wi-Fi fingerprinting system; Wi-Fi fingerprinting technique; hybrid algorithm architecture; hybrid classifier; hybrid-based approach; indoor positioning; k-NN algorithm; naive Bayes classifier; support vector machines; Accuracy; Algorithm design and analysis; Classification algorithms; Computer architecture; Fingerprint recognition; IEEE 802.11 Standards; Support vector machines; Hybrid Algorithm; IEEE 802.11 technology; Indoor Positioning System; NBC; SVM; Wi-Fi Fingerprinting; k-NN;

fLanguage

English

Publisher

ieee

Conference_Titel

ICT Convergence (ICTC), 2012 International Conference on

Conference_Location

Jeju Island

Print_ISBN

978-1-4673-4829-4

Electronic_ISBN

978-1-4673-4827-0

Type

conf

DOI

10.1109/ICTC.2012.6386791

Filename

6386791