Abstract
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.
Highlights
The rapid development of wireless communication techniques, micro-electronics techniques and embedded computation techniques makes Wireless Sensor Networks (WSNs) being applied in many fields [1,2,3,4]
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
Some sensor nodes may be isolated from the WSNs for a short or long time due to the influences of surrounding environment such as mountains and obstacles, which results that the sensor data of these nodes may be lost
Summary
The rapid development of wireless communication techniques, micro-electronics techniques and embedded computation techniques makes Wireless Sensor Networks (WSNs) being applied in many fields [1,2,3,4]. It adopts linear regression model to describe the spatial correlation of sensor data among different sensor nodes and uses the multiple neighbor nodes’ data jointly rather than independently to estimate the missing data. It can achieve a good estimation effect for the missing data, even for the sensor data of changing irregularly which appears often in WSNs. The performance of the algorithm proposed in this paper is evaluated through extensive experiments on two real-world datasets and compared with the other missing data estimation algorithm.
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