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Wireless Communications and Mobile Computing
Volume 2017 (2017), Article ID 1268515, 11 pages
Research Article

Dealing with Insufficient Location Fingerprints in Wi-Fi Based Indoor Location Fingerprinting

1School of Computer Science and Engineering, Southeast University, Nanjing, China
2College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Correspondence should be addressed to Kai Dong

Received 28 April 2017; Accepted 18 June 2017; Published 9 August 2017

Academic Editor: Zhe Yang

Copyright © 2017 Kai Dong et al. 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.


The development of the Internet of Things has accelerated research in the indoor location fingerprinting technique, which provides value-added localization services for existing WLAN infrastructures without the need for any specialized hardware. The deployment of a fingerprinting based localization system requires an extremely large amount of measurements on received signal strength information to generate a location fingerprint database. Nonetheless, this requirement can rarely be satisfied in most indoor environments. In this paper, we target one but common situation when the collected measurements on received signal strength information are insufficient, and show limitations of existing location fingerprinting methods in dealing with inadequate location fingerprints. We also introduce a novel method to reduce noise in measuring the received signal strength based on the maximum likelihood estimation, and compute locations from inadequate location fingerprints by using the stochastic gradient descent algorithm. Our experiment results show that our proposed method can achieve better localization performance even when only a small quantity of RSS measurements is available. Especially when the number of observations at each location is small, our proposed method has evident superiority in localization accuracy.