Optimizing Traffic Engineering in IoT and 5G Networks Using Advanced AI and PSO Techniques

The evolution of Internet of Things (IoT) and 5G networks has introduced unprecedented challenges in traffic management due to the sheer volume and dynamic nature of data transmission. Traffic engineering plays a crucial role in ensuring the efficiency and reliability of these modern networks, yet traditional methods often struggle to adapt to their dynamic and unpredictable nature. This paper addresses these challenges by proposing a novel hierarchical traffic engineering framework that integrates the synergistic capabilities of Particle Swarm Optimization (PSO) and Machine Learning (ML) techniques to enhance traffic management in IoT and 5G networks. The proposed framework operates in two interconnected layers: the lower layer leverages PSO for real-time optimization of network resources, focusing on dynamic adjustments to routing paths, bandwidth allocation, and other parameters to minimize latency, packet loss, and maximize throughput. The upper layer employs ML to analyze historical traffic data and predict future traffic patterns, enabling proactive management of network resources. This integrated dual-layer approach ensures optimal resource utilization and adherence to Quality of Service (QoS) requirements. Extensive simulations demonstrate that the proposed method significantly reduces latency, enhances throughput, and minimizes packet loss compared to traditional traffic engineering approaches. By harnessing the strengths of both PSO and ML, this framework offers a scalable and adaptive solution to the complexities of modern communication networks, contributing significantly to the state of the art in network traffic management and providing major implications f-or enhancing the efficiency and reliability of IoT and 5G networks.