Intelligent Spectrum Resource Management Integrating Time and Space & Frequency Domain Sensing Data

Edge computing is a promising model for building the IIoT because it transfers computationally intensive tasks from devices with limited resources to servers in the network's periphery, where they are more robustly powered. To fulfil the quality-of-service demands of different applications, it is important to effectively manage the available spectrum resources, taking into account the spectrum's restrictions, the battery's health, and the characteristics of the changes in the spectrum. Using time-space-frequency interference analysis to optimise variables, thinking motors to deduce inactive channels from backup channel lists and training motors for selecting the most suitable backup channels based on past data this study suggests a biological algorithm-based platform for intelligent flexible spectrum resource management. Researchers put the proposed spectrum resource management system through its paces by tracking variables such as energy usage, connection maintenance likelihood, the quantity of IoT devices, the amount of spectrum handoffs, and optimally transmitted parameters across all traffic scenarios. The outcomes show that the suggested system outperforms the current spectrum supply organization purposes.