Rendez-Vous Based Drift Diagnosis Algorithm for Sensor Networks Toward In Situ Calibration

In recent years, low-cost sensors have raised strong interest for environmental monitoring applications. These instruments often suffer from degraded data quality. Notably, they are prone to drift. It can be mitigated with costly periodic calibrations. To reduce this cost, in situ calibration strategies have emerged, enabling the recalibration of instruments while leaving them in the field. However, they rarely identify which instruments actually need a calibration because of drift, so that in situ calibration may instead degrade performances. Therefore, a novel drift detection algorithm is presented in this work, exploiting the concept of rendez-vous between measuring instruments. Its originality lies mainly in the comparisons of values determining the state of the instruments, for which the quality of the measurement results is taken into account. It defines the concept of compatibility between measurement results. A case study is developed, showing an accuracy of 88% for correct detection of drifting instruments. The results of the diagnosis algorithm are then combined with calibration approaches. Results show a significant improvement of the measurement results. Notably, an increase of 15% of the coefficient of determination of the linear regression between their true values and the measured values is observed with the correction and the error on the slope and on the intercept respectively is reduced by 50% and 60% at least. Note to Practitioners—In this paper, we investigate the problem of drift detection in sensor networks. This work was motivated by the fact that faulty nodes are rarely detected in existing in situ calibration algorithm prior to the correction of the instruments. Moreover, existing fault diagnosis algorithms for sensor networks do not specifically target drift and are often applicable to either (dense) static or mobile sensor networks but not both. We propose an algorithm designed for the detection of drift faults regardless of the type of sensor network and of the measurand. Specific attention is paid to the metrological quality of the measurement results used to carry out the diagnosis. The output of the algorithm provides information that can be exploited for the recalibration of faulty instruments. In future work, we will aim at providing tools and recommendations for the adjusment of the parameters of the diagnosis algorithm but also more elaborated approaches based on the results of our diagnosis algorithm to calibrate faulty nodes.