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A new online anomaly learning and detection for large-scale service of Internet of Thing
Wang JP(王军平); JUNPING WANG
Source PublicationPersonal and Ubiquitous Computing
2015-08-23
Volume19Issue:7Pages:1021–1031
AbstractThe online anomaly detection has been propounded as the key idea of monitoring fault of large-scale sensor nodes in Internet of Things. Now, the exciting progresses of research have been made in online anomaly detection area. However, the highly dynamic distributing character of Internet of Things makes the anomaly detection scheme difficult to be used in online manner. This paper presents a new online anomaly learning and detection mechanism for large-scale service of Internet of Thing. Firstly, our model uses the reversible-jump MCMC learning to online learn anomaly-free of dynamics network and service data. Next, we perform a structural analysis of IoT-based service topology by network utility maximization theory. The results of experiment demonstrate the method accuracy in forecasting dynamics network and service structures from synthetic data.
KeywordInternet Of Thing Predictive Manufacturing Online Anomaly Learning And Detection
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12215
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorJUNPING WANG
AffiliationLaboratory of Precision Sensing and Control Center, Institute of Automation, Chinese Academy
Recommended Citation
GB/T 7714
Wang JP,JUNPING WANG. A new online anomaly learning and detection for large-scale service of Internet of Thing[J]. Personal and Ubiquitous Computing,2015,19(7):1021–1031.
APA Wang JP,&JUNPING WANG.(2015).A new online anomaly learning and detection for large-scale service of Internet of Thing.Personal and Ubiquitous Computing,19(7),1021–1031.
MLA Wang JP,et al."A new online anomaly learning and detection for large-scale service of Internet of Thing".Personal and Ubiquitous Computing 19.7(2015):1021–1031.
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