An Online Anomaly Learning and Forecasting Model for Large-Scale Service of Internet of Thing
Wang JP(王军平); JUNPING WANG
2014-10
会议名称International Conference on Identification, Information & Knowledge in the Internet of Things
会议录名称International Conference on Identification, Information & Knowledge in the Internet of Things
会议日期2014-10-17
会议地点BEIJING
摘要The 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.
关键词Internet Of Things Service Delivery Online Anomaly Learning And Detection
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12343
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者JUNPING WANG
推荐引用方式
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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