A new online anomaly learning and detection for large-scale service of Internet of Thing. Issue 7 (October 2015)

• Record Type: Journal Article
• Authors: Wang, JunPing; Kuang, Qiuming; Duan, ShiHui
• Is Part Of: Personal and ubiquitous computing. Volume 19:Issue 7(2015)
• Page Start: 1021
• Page End: 1031
• Publication Date: 2015-10
• Subjects: Mobile computing -- Periodicals; Portable computers -- Periodicals; Human-computer interaction -- Periodicals; 004.16
• DOI: 10.1007/s00779-015-0874-8
• Languages: English
• ISSNs: 1617-4909

Abstract

Abstract 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.