Derin özelliklere sahip enerji verimli bir SDN-ıot mimarisi IoT destekli akıllı şehir için öğrenme tabanlı trafik tahmini

Thesis proposes an energy-efficient SDN-IoT architecture tailored for IoT-enabled smart cities. This architecture addresses the challenges of resource optimization and energy consumption management in the context of diverse IoT devices and dynamic traffic patterns. The architecture provides a framework for efficient network management and facilitates sustainable operation in smart city environments. A modified SVM (Support Vector Machine) algorithm is introduced and integrated into the proposed architecture for traffic prediction. By enhancing the traditional SVM algorithm with specific modifications tailored for IoT-generated data, the thesis contributes to improving the accuracy and reliability of traffic prediction in smart city networks. The modified SVM algorithm can effectively capture complex patterns and variations, enabling precise anticipation of traffic demands. The research presents a comprehensive evaluation of the proposed architecture's performance. Through extensive simulations and practical deployments in a smart city testbed environment, the paper assesses the energy efficiency, resource utilization, and predictive accuracy achieved by the architecture