Two-Level Sensor Self-Calibration Based on Interpolation and Autoregression for Low-Cost Wireless Sensor Networks

The increasing use of low-cost sensors in monitoring the surrounding environment requires efficient handling of sensor drift and sensor errors. Therefore, there is a pressing need to develop lightweight methods to determine and calibrate the sensor's readings accurately. This article focuses on the calibration of low-cost sensors using lightweight techniques to effectively detect and correct sensor drifts. The proposed approach combines clustering, which offloads computational burdens to cluster heads, with temporal and spatial estimation among neighboring sensors, such as inverse distance weighting (IDW). Additionally, autoregression (AR) and interquartile range (IQR) techniques are employed to monitor the stability of sensor readings based on the previous measurements. Through simulation experiments using a dataset from the Intel Berkeley Research Laboratory (IBRL), the effectiveness of the proposed method in reliably detecting and improving the sensor is demonstrated. These findings contribute to advancing sensor calibration techniques for low-cost sensors, enhancing their reliability and accuracy in environmental monitoring applications.