Adaptive Correction for Sensor Deviation Data of Monitoring

With the rapid development of Internet of Things (IoT), sensors play an indispensable role in the field of monitoring. In monitoring, the data collected by sensors as a part of the monitoring and control systems, and the data quality directly impacts the accuracy of the analysis. However, some factors such as environmental changes, node failures and structure damage can result in sensor abnormalities like frequent topology changes, communication failures, which greatly reduces sensor data reliability. To overcome this challenge, we propose to utilize the mathematical models to adaptively correct the deviated sensors data. Firstly, an initial evaluation of abnormal sensor is made based on the changing trends. Then, mathematical models are formulated for each sensor based on the monitoring data, and these models are employed to rectify the deviations in the monitoring data. Moreover, the corrected data is validated based on the observed trends. Experiments are executed based on sensors data collected from a real application scenario. The results confirm that the proposed correction method can significantly correct the sensors deviation data of monitoring.