Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support Vector Data Description
Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Estimating the Support of a High-Dimensional Distribution
Neural Computation
An online support vector machine for abnormal events detection
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
ACM Computing Surveys (CSUR)
SensorScope: Application-specific sensor network for environmental monitoring
ACM Transactions on Sensor Networks (TOSN)
Ensuring high sensor data quality through use of online outlier detection techniques
International Journal of Sensor Networks
IEEE Transactions on Information Forensics and Security
Outlier Detection Techniques for Wireless Sensor Networks: A Survey
IEEE Communications Surveys & Tutorials
Structured One-Class Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Communications Magazine
Hi-index | 0.00 |
Low quality sensor data limits WSN capabilities for providing reliable real-time situation-awareness. Outlier detection is a solution to ensure the quality of sensor data. An effective and efficient outlier detection technique for WSNs not only identifies outliers in a distributed and online manner with high detection accuracy and low false alarm, but also satisfies WSN constraints in terms of communication, computational and memory complexity. In this paper, we take into account the correlation between sensor data attributes and propose two distributed and online outlier detection techniques based on a hyperellipsoidal one-class support vector machine (SVM). We also take advantage of the theory of spatio-temporal correlation to identify outliers and update the ellipsoidal SVM-based model representing the changed normal behavior of sensor data for further outlier identification. Simulation results show that our adaptive ellipsoidal SVM-based outlier detection technique achieves better detection accuracy and lower false alarm as compared to existing SVM-based techniques designed for WSNs.