Method for Dynamic Subchannel Selection in Heterogeneous Internet of Things Environments

Purpose: The article addresses the task of dynamic subchannel selection in heterogeneous Internet of Things (IoT) networks, considering network parameter changes and the limited computational resources of devices. The subject of the study: Heterogeneous IoT networks that utilize various data transmission technologies. Methods used: The study employs a reinforcement learning method for dynamic subchannel selection based on the analysis of historical data and the current network state. A tug-of-war algorithm is also used for resource allocation among subchannels. Results: A method for dynamic subchannel selection has been developed, which allows for consideration of the probability of successful data transmission, subchannel usage frequency, and failure probability, thereby balancing transmission efficiency and computational costs. Theoretical /Practical relevance: The practical significance of the results lies in improving the performance and reliability of heterogeneous IoT networks under high load and with limited device resources.