Systems, Man, and Cybernetics

 

Home

• [P430] Finding Abnormal Events in Home Sensor Network Environment Using Correlation Graph
  Author(s): Huey-Ming Lee, Ching-Hao Mao
  
  

  Abstract
  Anomaly detection in sensor network seems a challengeable when encountering the limitation of the energy requirement and dynamics environments. It is critical to rapidly analyze and identify the abnormally events among the extremely volume data. Using correlation graph representation to correlate the events generated by sensor networks is capable to find the intentional dependency behavior's insight for detecting home sensor network abnormally events. In this study, we proposed an anomaly detection mechanism based on correlation graphs of sensor networks for rapidly identifying abnormal home events. The proposed mechanism which makes the following contribution: (a) it is automatically identify the abnormal event under home sensor network environment (b) it eliminates irrelevant events for saving the computation power (c) it is easily to apply on different machine learning classifiers for enhancement. The evaluation from Intel Berkeley Research lab sensor network data set. The proposed mechanism performs well in sensor events elimination and abnormal event detection.

  
   

Full Text (PDF)