A Spatial Correlation Based Adaptive Missing Data Estimation Algorithm in Wireless Sensor Networks

In wireless sensor networks, the missing of sensor data is inevitable due to the inherent characteristic of wireless sensor networks, and it causes many difficulties in various applications. To solve the problem, the missing data should be estimated as accurately as possible. In this paper, an adaptive missing data estimation algorithm is proposed based on the spatial correlation of sensor data. It adopts multiple regression model to estimate the missing data with the data of multiple neighbor nodes jointly rather than independently, which makes its estimation performance stable and reliable. In addition, for different missing data, it can adjust the estimation equation adaptively to capture the dynamic correlation of sensor data. Thereby, it can estimate the missing data more accurately. Further more, it can also give the confidence interval of each missing data for the given confidence level, which is helpful greatly for users. Experimental results on two real-world datasets show that the proposed algorithm can estimate the missing data accurately.