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Continuous discovery of co-location contexts from Bluetooth data

Nguyen, Thuong, Gupta, Sunil, Venkatesh, Svetha and Phung, Dinh 2015, Continuous discovery of co-location contexts from Bluetooth data, Pervasive and mobile computing, vol. 16, Part B, pp. 286-304, doi: 10.1016/j.pmcj.2014.12.005.

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Title Continuous discovery of co-location contexts from Bluetooth data
Author(s) Nguyen, Thuong
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Journal name Pervasive and mobile computing
Volume number 16
Season Part B
Start page 286
End page 304
Total pages 19
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-01
ISSN 1574-1192
Keyword(s) Co-location context
Incremental
Indian buffet process
Nonparametric
Particle filter
Science & Technology
Technology
Computer Science, Information Systems
Telecommunications
Computer Science
SENSOR NETWORKS
RECOGNITION
Summary The discovery of contexts is important for context-aware applications in pervasive computing. This is a challenging problem because of the stream nature of data, the complexity and changing nature of contexts. We propose a Bayesian nonparametric model for the detection of co-location contexts from Bluetooth signals. By using an Indian buffet process as the prior distribution, the model can discover the number of contexts automatically. We introduce a novel fixed-lag particle filter that processes data incrementally. This sampling scheme is especially suitable for pervasive computing as the computational requirements remain constant in spite of growing data. We examine our model on a synthetic dataset and two real world datasets. To verify the discovered contexts, we compare them to the communities detected by the Louvain method, showing a strong correlation between the results of the two methods. Fixed-lag particle filter is compared with Gibbs sampling in terms of the normalized factorization error that shows a close performance between the two inference methods. As fixed-lag particle filter processes a small chunk of data when it comes and does not need to be restarted, its execution time is significantly shorter than that of Gibbs sampling.
Language eng
DOI 10.1016/j.pmcj.2014.12.005
Field of Research 080109 Pattern Recognition and Data Mining
0805 Distributed Computing
1702 Cognitive Science
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076881

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