An Online Anomaly Learning and Forecasting Model for Large-Scale Service of Internet of Thing
文献类型：会议论文
| 作者 | Wang JP(王军平) ; JUNPING WANG
|
| 出版日期 | 2014-10 |
| 会议日期 | 2014-10-17 |
| 会议地点 | BEIJING |
| 关键词 | Internet Of Things Service Delivery Online Anomaly Learning And Detection |
| 英文摘要 | 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. |
| 会议录 | International Conference on Identification, Information & Knowledge in the Internet of Things
 |
| 源URL | [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]. 见:. BEIJING. 2014-10-17. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。