A Multi-Layer Strategy for Reducing Network Congestion and Enhancing Performance in 5G IoT Networks

The rapid proliferation of Internet of Things (IoT) devices poses significant challenges to network performance, particularly in terms of congestion due to limited network resources. Advances in cellular technology, especially 5G, offer promising solutions to these issues. However, as the number of IoT nodes continues to grow, networks are increasingly at risk of overload, leading to delays, packet loss, and instability. To address these challenges, this paper proposes a multi-layer optimization strategy aimed at enhancing network performance and scalability within 5G IoT networks. The approach involves simulating the uplink communication channel between IoT nodes and the base station (BS), followed by the formulation of a multi-objective optimization problem that considers the number of IoT nodes in a cell and the constraints of 5G cellular networks. This problem is solved to ensure scalability while minimizing performance degradation. The optimization strategy identifies the optimal BS downlink waiting time, transmission power, and IoT device data rates, using a discrete network simulator to evaluate these parameters. The results demonstrate that the proposed method effectively reduces network congestion and is more energy-efficient compared to existing static clustering systems, the mesh schemes, the Fair and Scalable Relay Control (FSRC) and the Spreading Factor Partition Based Clustering and Relay (SFPCR) schemes.