A Skeleton Pattern Representation Method for Anomaly Detection in Wireless Sensor Networks
Yanping Chen, Ping Yang, Cong Gao, Zhongmin Wang, Zhong Wu Yu · 2019
Anomaly detection of time series in wireless sensor networks has gained significant attention. Researchers employ representation techniques to reduce the dimensionality of time series. The Piecewise Aggregate Pattern Representation (PAPR) method vertically divides the time series and represents as patterns. However, the computational cost of the PAPR method for anomaly detection is high. To overcome this issue, we propose a time series representation method for anomaly detection called Skeleton Pattern Representation for anomaly detection (SPR-AD). An improved Perceptually Important Point (iPIP) identification algorithm is devised to transform the subsequence into a skeleton with PIPs. Then the skeleton is divided into several equal-sized subspaces in the amplitude domain. Statistical information of the PIPs located in subspaces is constructed to represent the skeleton as a pattern. Based on the two-phase representation scheme, anomaly is determined by anomaly scores which are based on the of similarity among pattern. The experimental results demonstrate the performance of the SPR-AD method is better in terms of computational cost and detection accuracy than the PAPR method.