Abstract
Wireless sensor networks (WSNs) have been increasingly applied for environmental monitoring in recent years. However, the sensor data drift is a serious issue affecting the reliability of monitoring system. Based on the assumption that the neighboring nodes have correlated measurements, this paper presents a novel algorithm using constrained extreme learning machine and Kalman filter (CELM-KF) for tracking and calibrating drift of sensor data. CELM-KF has two phases: training phase and calibrating phase. In the training phase, the wavelet denoising method is used for data preprocessing. Then the cluster head trains the model of the constrained extreme learning machine (CELM) using the measurements of its neighbors to obtain the prediction data of the target sensor. In the calibrating phase, we track and correct the data drift of target sensor using Kalman filter. To evaluate the performance of CELM-KF, simulation experiments on different datasets are conducted, and two parameters including decision coefficient and mean square error (MSE) of CELM-KF are compared with those of existing algorithms. The simulation results show that CELM-KF can successfully calibrate the sensor data drifts.
Highlights
Environmental monitoring is a typical application of wireless sensor network (WSN) [1], [2]
In the field of environmental monitoring, sensors are often deployed in unattended places for a long time, which makes data drift become a serious issue affecting the reliability of sensor data [7]–[10]
We propose a blind calibration algorithm (CELM-KF) using constrained extreme learning machine and Kalman filter to track and calibrate sensor drift
Summary
Environmental monitoring is a typical application of wireless sensor network (WSN) [1], [2]. The reference information is generally high-fidelity measurement of an observed quantity [14]–[16] Another type of non-blind calibration technique is based on a manual calibration of a set of sensors, and other sensors are calibrated by those calibrated manually [15]. Li: Drift Calibration Using CELM-KF in Clustered WSNs without ground-truth data This calibration method is called blind calibration [17]. Many other works apply different prediction functions in the similar framework, including support vector regression (SVR) [20] and Kriging interpolation [21] These methods exploit the correlation of sensors, and Kalman filter is used to track the drift. We propose a blind calibration algorithm (CELM-KF) using constrained extreme learning machine and Kalman filter to track and calibrate sensor drift.
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