ROUTING IN THE INTERNET OF THINGS USING COMPUTATIONAL INTELLIGENCE TECHNOLOGIES

Abstract

The Internet of Things (IoT) is one of the technologies of modern digital transformation, which provides interaction of devices, objects and sensors, that requires efficient and reliable data routing in real time. Traditional routing algorithms often do not take into account the uncertainties inherent in the IoT environment, such as unstable connection, limited energy resources and variability of network parameters, which necessitates the need for new intelligent approaches. In this regard, the implementation of computational intelligence technologies, in particular fuzzy controllers, artificial neural networks and evolutionary algorithms, which are able to provide flexible and efficient management of data flows, is becoming increasingly relevant. The article discusses the application of computational intelligence technologies to solve the routing problem in IoT. Special attention is paid to hybrid approaches that combine the advantages of several intelligent methods to increase the efficiency of routing processes in a resource-constrained IoT environment. As part of the research, a fuzzy controller was developed in the MATLAB program, which provides decision-making on the selection of the optimal route based on the main network parameters. To increase the accuracy of the system, the fuzzy controller was transformed into a neuro-fuzzy system that combines the ability of fuzzy logic to handle uncertainty and the learning capabilities of artificial neural networks. Further optimization of the system was carried out using a genetic algorithm, which allowed automatically adjusting the parameters of fuzzy rules and membership functions to achieve the best routing performance. The presented system is an example of the effective application of intelligent methods in critical IoT tasks and opens up prospects for further research in the direction of next-generation networks.