Advancing 6G-IoT networks: Willow catkin packet transmission scheduling with AI and bayesian game-theoretic approach-based resource allocation

The rapid expansion of mobile broadband networks and the proliferation of Internet of Things (IoT) applications have substantially increased data transmission and processing demands. However, the application domains of IoT-enabled models often face resource limitations, requiring rapid responses, low latency, and large bandwidth, surpassing their inherent capabilities. To address these challenges, we propose a fishnet approach-based packet scheduling and resource allocation system, termed Fishnet-6G, to optimize network resource allocation in the proposed 6G networks. Initially, we constructed a Sierpinski Triangle-based network in a 6G-IoT environment, enhancing device connectivity. We utilize the Quantum Density Peak Clustering (QDPC) algorithm to perform clustering for IoT devices, establishing Cluster Head (CH) and Substitute CH (SUB CH) based on actual metrics. Furthermore, traffic prediction is achieved through two processes, grouping, and fair queue status, using the Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm with a variable sampling rate, resulting in well-organized packet scheduling. Subsequently, we perform optimal packet scheduling by employing the Willow Catkin Optimization (WCO) algorithm, and the scheduled packets are managed within a Fishing Net Topology to reduce energy consumption and system complexity. Finally, we allocate the scheduled packets to the desired resource blocks using the Bayesian Game-Theoretic Approach (BGTA). The proposed approach is implemented using Network Simulator-3.26, and the performance of the Fishnet-6G model is evaluated based on time, transmission rate, energy efficiency, average throughput, latency, and Packet loss rate. Numerical analysis demonstrates that Fishnet6G outperforms existing approaches across these metrics, showcasing its effectiveness in addressing the challenges of 6G-IoT networks.