Journal
IEEE COMMUNICATIONS MAGAZINE
Volume 58, Issue 1, Pages 40-46Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MCOM.001.1900283
Keywords
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Funding
- National Natural Science Foundation of China [61902445, 61872310]
- Fundamental Research Funds for the Central Universities of China [19lgpy222]
- Natural Science Foundation of Guangdong Province of China [2019A1515011798]
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To build the mechanism of a SelfDN is becoming an emerging direction for future IoT. The detection of device status, such as fault or normal, is a very fundamental module in SelfDN. In this article, we first review recent studies devoted to applying fault detection techniques in IoT networks. Taking the challenge of processing the real-valued IoT data into account, we propose a novel fault detection architecture for SelfDN. Under this architecture, we present an algorithm, named GBRBM-based deep neural network with auto-encoder (i.e., GBRBM-DAE) to transform the fault detection problem into a classification problem. The real-world trace-driven experimental results show that the proposed algorithm outperforms other popular machine learning algorithms, including linear discriminant analysis, support vector machine, pure deep neural network, and so on. Finally, we summarize some open issues of this study. We expect that this article will inspire successive studies on the related topics of SelfDN.
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