Abstract: On wireless sensor network, the missing data causes many difficulties in data analysis. Prior to data analysis, data preprocessing is necessary. Sensor network data has some change rules both in time and space. Existing imputation algorithms of missing values solve the problems only from a single point of view. In this paper, a imputation algorithm of missing values based on temporal and spatial correlation is proposed in order to make full use of the characteristics of space and time. It adopts regression model and improves BP neural network to estimate the missing values. Experiments show that this method can improve the imputation accuracy of missing values effectively.

Key words:  temporal and spatial correlation, missing values, sensor network, BP neural network