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An Online Anomaly Learning and Forecasting Model for Large-Scale Service of Internet of Thing
Wang JP(王军平); JUNPING WANG
2014-10
Conference NameInternational Conference on Identification, Information & Knowledge in the Internet of Things
Source PublicationInternational Conference on Identification, Information & Knowledge in the Internet of Things
Conference Date2014-10-17
Conference PlaceBEIJING
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 (NUM) theory. The results
of experiment demonstrate the method accuracy in forecasting
dynamics network and service structures from synthetic data.
KeywordInternet Of Things Service Delivery Online Anomaly Learning And Detection
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12343
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorJUNPING WANG
Recommended Citation
GB/T 7714
Wang JP,JUNPING WANG. An Online Anomaly Learning and Forecasting Model for Large-Scale Service of Internet of Thing[C],2014.
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