Uncertain sensor data prediction for wireless sensor networks

In wireless sensor networks, affected by sensor noise, sensor error, transmission error and other factors, data collected from different sensor nodes in the same sensor field are different, leading to uncertainty of the sensor data. This is a challenge for further data processing methods such as sensor data mining and query. To deal with the data uncertainty problem, an uncertain sensor data prediction method based on multiple variable principle component analysis (MVPCA) is proposed. The uncertain sensor data are first pretreated with MVPCA feature extraction to obtain the intrinsic feature from the uncertain data. A multiple regression prediction method based on correlation analysis is then applied to the feature data for modeling and prediction. Sensor data of a real wireless sensor network are used to estimate the method. The results show that the proposed method can efficiently predict uncertian sensor data with high accuracy.