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 missing data recovery tasks 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
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.