CalibrationTalk: A Farming Sensor Failure Detection and Calibration Technique

Sensor failure detection and calibration is an important issue for Internet of Things applications. Many smart applications fail due to inaccurate data produced by their sensors. To solve this issue, we propose the CalibrationTalk mechanism. We use smart agriculture as an example to show how CalibrationTalk automatically fixes the sensing accuracy problems in commercial farm operations. Besides manufacturing variation, hardware malfunction and aging, sensing inaccuracy may be caused by soil erosion of rain or irrigation, which changes the positions and the angles of the sensors inserted in the soil. The above sensor failure problems can be detected by CalibrationTalk. In this paper, we first extend our previous work to detect sensor failures. If the sensors become inaccurate due to aging or similar reasons, CalibrationTalk automatically calibrates the sensors in the farm fields. We conduct measurements and analytic modeling to investigate the performance of CalibrationTalk, which suggests that to detect a potential moisture sensor failure, it is appropriate to set the detection time as 30 minutes for moisture sensors. With this setup, true failure is detected while false detections rarely occur. For a detected sensor failure due to aging, CalibrationTalk can automatically build a calibration table. During the calibration table establishment period, the sensors still produce correct measured data and do not interfere with the normal operation of smart farming. The value of CalibrationTalk lies in its power of automation for sensor failure detection and calibration processes developed in this paper.