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Indexed by:期刊论文

Date of Publication:2017-02-01

Journal:COMPUTERS & ELECTRICAL ENGINEERING

Included Journals:SCIE、EI、Scopus

Volume:58

Page Number:327-336

ISSN No.:0045-7906

Key Words:Industrial Internet of Things (IloT); Sensor networks; Data stream; PCA; AOGE

Abstract: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.