A Two-step Automatic Calibration Method for Sensor Accuracy Management

Abstract

IoT technology has spread throughout the world and many applications have emerged that utilize large numbers of sensors. The concept of a sensor cloud, in which sensors are shared, has also emerged, and there is a need to manage and operate a large number of distributed sensors properly. However, in order for sensors to measure accurate values, periodic calibration is necessary, and performing this for all sensors is very costly and impractical. An automatic calibration method has been proposed as a method to calibrate a large number of sensors at once, taking advantage of the fact that the measurements of neighboring sensors take close values. However, these methods aim to find the optimal correction value, and it is impossible to know how accurate the correction value is. In this study, we propose a new automatic calibration method to keep the errors of all sensors sufficiently small. The proposed method uses a probability distribution to estimate the magnitude of error for each sensor, and periodically calibrates selected sensors based on this distribution to maintain the overall sensor accuracy at the required level. Through simulation evaluation, it is shown that the proposed method can maintain the accuracy of the sensors.