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Wireless Communications and Mobile Computing
Volume 2018, Article ID 2976751, 9 pages
Research Article

A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things

Department of Computer Engineering, Ajou University, Suwon 16499, Republic of Korea

Correspondence should be addressed to Young-Bae Ko;

Received 8 September 2017; Revised 9 January 2018; Accepted 18 January 2018; Published 19 February 2018

Academic Editor: Waleed Ejaz

Copyright © 2018 Sajida Imran and Young-Bae Ko. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.