DYNAMIC INTELLIGENT CHANNEL ASSIGNMENT MODEL WITH OPTIMIZED THROUGHPUT-BASED COGNITIVE UAV GUIDED SMART INTERNET OF THINGS ENVIRONMENT

The IoT technology allows numerous devices to link up with the Internet and exchange data smoothly. It is predicted that
shortly, there will be trillions of these devices connected. As a result, there is a growing demand for spectrum to deploy
these devices. Many of these devices operate on unlicensed frequency bands, leading to interference as these bands become
overcrowded. A new communication approach known as cognitive radio-based Internet of Things (CR IoT) is rapidly
emerging to address this issue and the spectrum scarcity. This involves integrating cognitive radio technology into IoT
devices, allowing for dynamic spectrum access, and overcoming interference problems. In current systems, a significant
portion of the spectrum designated for primary users (PU) may be underutilized, leaving room for secondary users (SU) to
utilize the spectrum. However, the main challenge is that SUs must continuously send packets until they find an available
channel in real-world conditions, resulting in excessive communication and packet loss. To overcome these kinds of
drawbacks in the network in this article dynamic intelligent channel allocation with optimized throughput-based cognitive
UAV guided network model is developed. The major categories that are concentrated in this model are UAV-based cognitive
IoT network construction, dynamic intelligent channel assignment model, and optimized throughput calculation process.
By utilizing these methods, we can achieve streamlined channel allocation and economical communication, ultimately
enhancing the performance of the UAV-guided CRN-based IoT environment. The implementation of this model is carried
out in MATLAB software and the parameters that are considered for performance analysis are network throughput, power
utilization, energy efficiency, data delivery ratio, and average delay.