An Online Anomaly Learning and Forecasting Model for Large-Scale Service of Internet of Thing

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	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	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
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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