Real-time Detection Algorithm for Anomaly Data in Sensor Networks
Luo Li · Jisuanji fangzhen · 2007
Anomaly data detection is of great practical application meaning for sensor networks. Variable-self linear regression was adopted and a predictive model including its’ predictive method over data stream in sensor networks was proposed. An adaptive strategy for the predictive model was proposed in order to decrease the rate of predictive errors when prediction was defected. A method of detecting anomaly data was give based on this model,which was used to detect anomaly events and compress data via computing the specific value between current prediction error and the average of prediction error. This specific value and threshold was compared for the sake of detecting whether the current data was anomaly or not .On the basis of it,an anomaly events detection and data compression algorithm was proposed. Analytical and experimental results show that this algorithm is effective in anomaly events detection and data compression.