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, a k-nearest neighbor based missing data estimation algorithm is proposed based on the temporal and spatial correlation of sensor data. It adopts the linear regression model to describe the spatial correlation of sensor data among different sensor nodes, and utilizes the data information of multiple neighbor nodes to estimate the missing data jointly rather than independently, so that a stable and reliable estimation performance can be achieved. Experimental results on two real-world datasets show that the proposed algorithm can estimate the missing data accurately.
The K -nearest neighbor estimation method REF is applied to describe the spatial correlation among the sensor data of different sensor nodes by using the linear regression model, and it uses the data information of each neighboring node to estimate the missing data.
K-Nearest Neighbor Based Missing Data Estimation Algorithm in Wireless Sensor Networks
Wirel. Sens. Netw.
Wirel. Sens. Netw.
Computer Science