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A Deep Learning Approach for Blind Drift Calibration of Sensor Networks
Temporal drift of sensory data is a severe problem impacting the data quality
of wireless sensor networks (WSNs). With the proliferation of large-scale and
long-term WSNs, it is becoming more important to calibrate sensors when the
ground truth is unavailable. This problem is called "blind calibration". In
this paper, we propose a novel deep learning method named projection-recovery
network (PRNet) to blindly calibrate sensor measurements online. The PRNet
first projects the drifted data to a feature space, and uses a powerful deep
convolutional neural network to recover the estimated drift-free measurements.
We deploy a 24-sensor testbed and provide comprehensive empirical evidence
showing that the proposed method significantly improves the sensing accuracy
and drifted sensor detection. Compared with previous methods, PRNet can
calibrate 2x of drifted sensors at the recovery rate of 80% under the same
level of accuracy requirement. We also provide helpful insights for designing
deep neural networks for sensor calibration. We hope our proposed simple and
effective approach will serve as a solid baseline in blind drift calibration of
sensor networks