Nehodenko O. Methods and models of information processing in IοT networks based on spline filters

The improvement of existing methods of data processing in IoT networks on the basis of spline filters was formulated as a currently important scientific task and completed in the dissertation. The aim of the dissertation lies in the improvement of the indicators of effectiveness of the Internet of Things information control system. A special place in modern information technologies is dedicated to the Internet of Things, the main idea of which presupposes the possibility of connecting every object to the network, processing data coming from the surrounding environment, and the exchange of this data. In order to provide connection between a large number of devices in the Internet of Things information control system, wired and wireless technologies are used. The latter support a significant increase of data transfer rate and an enlarged number of users. At the same time, the increased number of connected devices within one information control system requires broadening the frequency band which is irrational due to high cost of additional bands. The analysis of existing approaches to simultaneous data transfer in the common range has proven the use of spline filters to be reasonable. The use of splines in information systems is highly promising taking into account the significantly lower requirements to the hardware and software components of the control system. This paper demonstrates the effectiveness of using mathematical model of information signals on the basis of fundamental trigonometric splines that, as opposed to the existing ones, are represented by one analytical look and also allow for real-time data processing. The implementation of the improved method of data processing between the devices of one control system and the methodology of data processing at the point of marking communication channels on the basis of spline filters, which is different from the existing one because of the use of fundamental trigonometric splines as basic functions, allow for the twice increased accuracy of the calculation of interpolation data of fundamental trigonometric splines, and for the 6 % increased indicator of information network noise resistance with a larger number of connected devices, and also for a faster algorithm of data processing in IoT networks.