Published Paper Details:

The exponential growth of Internet of Things (IoT) devices has introduced significant challenges in managing network resources efficiently while ensuring Quality of Service (QoS). The integration of Software-Defined Networking (SDN) with IoT environments has emerged as a promising solution due to its centralized control, programmability, and adaptability. However, dynamic traffic patterns, latency-sensitive applications, and heterogeneous network demands exacerbate the need for intelligent load-balancing mechanisms. This research identifies key limitations in existing QoS-aware load-balancing techniques in SDN-based IoT networks, such as poor scalability, high response time, and uneven resource utilization. To address these challenges, the study proposes a novel framework that focuses on improving load distribution across cloud, fog, and edge infrastructures while meeting QoS requirements. The methodology involves the classification of IoT traffic based on priority and implementing efficient routing and resource allocation strategies using SDN controllers. By leveraging centralized programmability, the proposed approach dynamically allocates resources and reroutes traffic to mitigate congestion and optimize network performance. The findings demonstrate that the proposed framework significantly reduces latency, enhances throughput, and ensures reliability for time-sensitive IoT applications. Comparisons with existing approaches reveal notable improvements in scalability and overall efficiency, making the framework suitable for real-world IoT deployments such as smart cities, healthcare systems, and industrial automation. This research contributes to the advancement of intelligent, QoS-aware load-balancing solutions in SDN-IoT networks and lays the groundwork for future optimizations in network resource management.