Blind Drift Calibration of Sensor Networks Using Multi-Output Gaussian Process

In modern wireless sensor network (WSN) applications, the long-term drift of sensors is becoming a challenge for the accuracy and reliability of the data. In this paper, we propose a blind calibration algorithm for WSNs. The algorithm models the spatial and temporal correlation with multi-output Gaussian process (MOGP) from a long-term perspective. It can estimate the drift with mean square error (MSE) less than 10% on two real-world datasets, which outperforms other novel blind calibration algorithms, especially when the signal contains longterm trend.