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论文类型：期刊论文

发表时间：2017-02-01

发表刊物：COMPUTERS & ELECTRICAL ENGINEERING

收录刊物：SCIE、EI、Scopus

卷号：58

页面范围：327-336

ISSN号：0045-7906

关键字：Industrial Internet of Things (IloT); Sensor networks; Data stream; PCA; AOGE

摘要：As the significant component in Industrial Internet of Things (lIoT), sensor networks have been applied widely in many fields. However, concept drift in data stream produced in sensor networks always brings great difficulty for the robustness of data processing. To solve the problem, we propose a novel concept drift detection method based on angle optimized global embedding (AOGE) and principal component analysis (PCA) for data stream learning in sensors networks. AOGE and PCA analyze the principal components through the projection variance and the projection angle in the subspace, respectively. And then the occurrence of concept drift is determined by observing the change of subspace for each data stream patch. The experiments in synthetic datasets and Intel Lab data demonstrate witness the effectiveness of our method. (C) 2016 Published by Elsevier Ltd.