A Multiple-Regression-Model-Based Missing Values Imputation Algorithm in Wireless Sensor Network

In wireless sensor network, the missing of sensor data is inevitable due to the inherent characteristic of wireless sensor network, and it causes many difficulties in various applications. To solve the problem, the best way is to estimate the missing data as accurately as possible. In this paper, a multiple-regression-model-based missing values imputation algorithm is proposed. It first adopts the multiple linear regression model to estimate the missing data both on temporal dimension and spatial dimension. Then, it assigns the weight coefficients to the two estimated values computed respectively on temporal dimension and spatial dimension according to the goodness-of-fit, and then uses the weighted average of the two values as the final estimated value. Since the algorithm estimates the missing data with the data of multiple neighbor nodes jointly rather than independently, its estimation performance is more stable and reliable. Experimental results on two real-world datasets show that the proposed algorithm can estimate the missing data accurately.