Name
Abstract | ||
|---|---|---|
This paper targets the identification of outlying sensors (that is, outlying reading sensors) and the detection of the reach of events in sensor networks. Typical applications include the detection of the transportation front line of some vegetation or animalcule's growth over a certain geographical region. We propose and analyze two novel algorithms for outlying sensor identification and event boundary detection. These algorithms are purely localized and, thus, scale well to large sensor networks. Their computational overhead is low, since only simple numerical operations are involved. Simulation results indicate that these algorithms can clearly detect the event boundary and can identify outlying sensors with a high accuracy and a low false alarm rate when as many as 20 percent sensors report outlying readings. Our work is exploratory in that the proposed algorithms can accept any kind of scalar values as inputs¿a dramatic improvement over existing work, which takes only 0/1 decision predicates. Therefore, our algorithms are generic. They can be applied as long as "events¿ can be modeled by numerical numbers. Though designed for sensor networks, our algorithms can be applied to the outlier detection and regional data analysis in spatial data mining. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TKDE.2007.1067 | IEEE Trans. Knowl. Data Eng. |
Keywords | Field | DocType |
large sensor network,outlying sensor identification,boundary data detection,sensor network,roc curve analysis.,percent sensor,event boundary detection,sensor networks,outlying reading,outlying sensor,outlier detection,outlying reading sensor,localized outlying,event boundary,data analysis,object recognition,artificial neural networks,algorithm design and analysis,simulation,face detection,roc curve,transportation,false alarm rate,discrete event simulation,computational modeling,gaussian distribution,vegetation,base stations,computer science | Overhead (computing),Data mining,Anomaly detection,Computer science,Sensor array,Outlier,Artificial intelligence,Constant false alarm rate,Face detection,Wireless sensor network,Machine learning,Discrete event simulation | Journal |
Volume | Issue | ISSN |
19 | 8 | 1041-4347 |
Citations | PageRank | References |
94 | 3.51 | 24 |
Authors | ||
6 | ||