Recovery of Missing Sensor Data with GRNN-based Cascade Scheme
Roman Tkachenko, Ivan Izonin, Ivanna Dronyuk, Mykola Logoyda, Pavlo Tkachenko
Abstract
Background: Today, IoT-based systems are widely used in various applications. Intellectual analysis of the data collected by IoT devices is an important task for the efficient and successful functioning of such systems. In particular, the reliability of such kind of analysis has greatly influenced the ability to partially or fully automate certain processes or subsystems. However, imperfect devices of data collection, transportation errors, etc. lead to gaps in the data. Such incompetency makes an effective intellectual data analysis for the specific tasks impossible. That is why there must be a scientific solution to this problem of effectively filling the gaps in the data collected by the IoT sensors. Methods: The authors propose a new prediction method for solving this problem based on the use of General Regression Neural Networks (GRNN). Results: The possibility of approximation and partial elimination of the error of this computational intelligence tool has been analytically proved. A prediction method based on the use of GRNN was developed. The optimal parameters of the developed method were selected. The simulation of its work was performed to solve the problem of recovery of missing sensor data in the air pollution monitoring dataset. Conclusion: The highest accuracy of the developed method in comparison with other methods of this class was experimentally established. All advantages and disadvantages are described. Perspectives of further research are outlined.