DocumentCode :
1300693
Title :
Tracking Mobile Users in Wireless Networks via Semi-Supervised Colocalization
Author :
Pan, Jeffrey Junfeng ; Pan, Sinno Jialin ; Yin, Jie ; Ni, Lionel M. ; Yang, Qiang
Author_Institution :
Facebook, Inc., Palo Alto, CA, USA
Volume :
34
Issue :
3
fYear :
2012
fDate :
3/1/2012 12:00:00 AM
Firstpage :
587
Lastpage :
600
Abstract :
Recent years have witnessed the growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labeled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters or access points. To solve this problem, we have developed a novel machine learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile users´ locations and the locations of access points. Our framework exploits both labeled and unlabeled data from mobile devices and access points. In our two-phase solution, we first build a manifold-based model from a batch of labeled and unlabeled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase colocalization to an online and incremental model that can deal with labeled and unlabeled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed on three different testbeds.
Keywords :
collaborative filtering; information retrieval; learning (artificial intelligence); mobile computing; mobility management (mobile radio); pattern clustering; wireless sensor networks; access points; collaborative filtering; graph based semisupervised learning; incremental model; labeled data; machine learning based approach; manifold based model; mobile client; mobile devices; mobile user location; offline training phase; online localization phase; semisupervised colocalization; signal transmitters; two-phase colocalization; unlabeled data; user location data; weighted k-nearest neighbor method; wireless sensor network; Data models; IEEE 802.11 Standards; Laplace equations; Manifolds; Mobile handsets; Robot sensing systems; Trajectory; AI applications.; Wireless sensor networks; colocalization; indoor localization; semi-supervised learning;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2011.165
Filename :
5989824
Link To Document :
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