A new online anomaly learning and detection 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

	A new online anomaly learning and detection for large-scale service of Internet of Thing; A new online anomaly learning and detection for large-scale service of Internet of Thing; A new online anomaly learning and detection for large-scale service of Internet of Thing; A new online anomaly learning and detection for large-scale service of Internet of Thing
	Wang JP(王军平); JUNPING WANG
发表期刊	Personal and Ubiquitous Computing ; Personal and Ubiquitous Computing ; Personal and Ubiquitous Computing ; Personal and Ubiquitous Computing
	2015-08-23 ; 2015-08-23 ; 2015-08-23 ; 2015-08-23
卷号	19 期号:7 页码:1021–1031
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 theory. The results of experiment demonstrate the method accuracy in forecasting dynamics network and service structures from synthetic data.; 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 theory. The results of experiment demonstrate the method accuracy in forecasting dynamics network and service structures from synthetic data.; 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 theory. The results of experiment demonstrate the method accuracy in forecasting dynamics network and service structures from synthetic data.; 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 theory. The results of experiment demonstrate the method accuracy in forecasting dynamics network and service structures from synthetic data.
关键词	Internet Of Thing Internet Of Thing Internet Of Thing Internet Of Thing Predictive Manufacturing Predictive Manufacturing Predictive Manufacturing Predictive Manufacturing Online Anomaly Learning And Detection Online Anomaly Learning And Detection Online Anomaly Learning And Detection Online Anomaly Learning And Detection
收录类别	SCI ; SCI ; SCI ; SCI
语种	英语 ; 英语 ; 英语 ; 英语
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12215
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
作者单位	Laboratory of Precision Sensing and Control Center, Institute of Automation, Chinese Academy
推荐引用方式 GB/T 7714	Wang JP,JUNPING WANG. A new online anomaly learning and detection for large-scale service of Internet of Thing, A new online anomaly learning and detection for large-scale service of Internet of Thing, A new online anomaly learning and detection for large-scale service of Internet of Thing, A new online anomaly learning and detection for large-scale service of Internet of Thing[J]. Personal and Ubiquitous Computing, Personal and Ubiquitous Computing, Personal and Ubiquitous Computing, Personal and Ubiquitous Computing,2015, 2015, 2015, 2015,19, 19, 19, 19(7):1021–1031, 1021–1031, 1021–1031, 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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