由于传感误差、传感噪声、传输错误等因素的影响，同一个传感区域内多个传感器节点的传感数据具有一定程度的差异，这种差异导致的区域不确定性传感数据给查询、预测等后续深层次的数据处理提出了严峻挑战.针对这类传感数据的预测问题，提出一种基于多变量主元分析（multiple variable principal component analysis,MVPCA）的不确定性传感数据预测方法. 通过MVPCA的特征提取这一预处理手段获得不确定性传感数据的本质特征，然后采用基于相关分析的多元回归方法对这些数据进行建模和预测. 实际传感数据的实验结果表明，该方法能有效解决不确定性传感数据的预测问题.
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 featrure 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.
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