RADIOELECTRONIC AND COMPUTER SYSTEMS

The subject of the study is the integration of artificial intelligence (AI) methods into software-defined networks (SDN) for adaptive control of access to limited resources within the infrastructure of Internet of Things (IoT) ecosystems. The goal of this work is to develop a model and architectural solution for a hybrid Cloud-SDN-IoT framework with embedded AI components, enabling the intelligent allocation of network and computing resources, and to experimentally validate the improvement of the fair distribution of a limited IoT resource across different traffic patterns in an emulation environment. The main tasks of the research are: 1) to analyze modern approaches to energy-efficient resource management and security in SDN-IoT networks; 2) to create the architecture of a hybrid Cloud-SDN-IoT framework that combines centralized SDN network control with the flexibility of cloud infrastructure; 3) to develop an experimental methodology using machine learning components to improve resource allocation and reduce load imbalance among competing clients; 4) to evaluate the system’s efficiency in relation to the stated objectives and the fair distribution of limited IoT resources by assessing the request distribution and the accuracy of detecting resource access violations. The paper proposes an improved three-layer SDN architecture model incorporating AI-based analytics: the IoT infrastructure layer, the SDN control layer, and the cloud application layer. The experimental part was implemented in a virtual Linux environment using Mininet and Ryu, where the trained AI model makes decisions about allocating the limited resource. The experimental results demonstrated that integrating the AI module into the SDN controller workflow increases the accuracy of detecting resource access violations, reduces load imbalance among clients, and improves the stability of real-time request distribution. Conclusions. The scientific novelty of the obtained results lies in the development of a reproducible hybrid Cloud-SDN-IoT architecture model that enables adaptive management of access to limited IoT node resources by combining centralized SDN control with AI-based predictive analytics. The AI-enabled control loop increased the average fairness accuracy of request distribution from 79.2% to 90.98%, an increase of 11.78 percentage points (14.87% relative), demonstrating improved proportional access to the limited IoT TokenServer API while preserving stable, real-time request regulation. The practical significance lies in the potential application of the proposed approach to optimize access to limited cloud services, APIs, energy resources, or IoT devices in smart city systems, healthcare, or industrial networks. Further research will focus on expanding the AI components with various machine learning models, forming new datasets, and conducting comparative evaluations of each model’s effectiveness in dynamic SDN-IoT resource management and reproduction under real-world conditions.

software-defined networks; Internet of Things; artificial intelligence; resource management; cloud infrastructure; SDN controller; machine learning

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