In wireless sensor networks, missing 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 best way is to estimate the missing data as accurately as possible. In this paper, for the data of changing smoothly, a temporal correlation based missing data estimation algorithm is proposed, which adopts the cubic spline interpolation model to capture the trend of data varying. Next, for the data of changing non-smoothly, a spatial correlation based missing data estimation algorithm is proposed, which adopts the multiple regression model to describe the data correlation among multiple neighbor nodes. Based on these two algorithms, an adaptive missing data estimation algorithm, called CIAM, is proposed for processing the missing data when the category of data changing is unknown. Experimental results on two realworld datasets show that the proposed algorithms can estimate the missing data accurately.
展开▼
