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Wireless Communications and Mobile Computing
Volume 2017 (2017), Article ID 6274824, 8 pages
Research Article

The Fusion Model of Multidomain Context Information for the Internet of Things

1College of Computer Science, Inner Mongolia University, Hohhot, China
2School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China

Correspondence should be addressed to Shuai Liu; nc.ude.umi@iauhsuil_sc

Received 20 August 2017; Accepted 11 October 2017; Published 13 November 2017

Academic Editor: Yin Zhang

Copyright © 2017 Bing Jia 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 Internet of Things aims to provide the user with deep adaptive intelligence services according to the user’s personalized characteristics. Most of the characteristics are presented in the form of high-level context. But it often lacks methods to obtain high-level context information directly in the Internet of Things. In this paper, so as to achieve the corresponding high-level context information using the specific low-level multidomain context directly obtained by different sensors in the Internet of Things, we present a machine learning method to construct a context fusion model based on the feature selection algorithm and the multiclassification algorithm. First, we propose a wrapper feature selection method based on the genetic algorithm to obtain a simpler and more important subset of the context features from the low-level multidomain context, by defining a suitable fitness function and a convergence condition. Then, we use the decision tree algorithm which is a multiclassification algorithm, based on the rules obtained by training the subset of context features, to determine which high-level context the record set of the low-level context information belongs to. Experiments confirm that the model can be used to achieve higher classification accuracy without more significant time consumption.