Implementing a smart city using internet of things based on wildwood combined with fractional order-based golden...

The fast expansion of Internet of Things (IoT) devices in urban environments has resulted in a dramatic increase in both the volume and complexity of data produced, necessitating the implementation of sophisticated data analytics and machine learning methodologies to fully realize the advantages of smart cities. The incorporation of IoT sensors and devices has facilitated the establishment of extensive, dynamic, and diverse networks, which present considerable challenges for data analysis and decision-making processes. In response to these challenges, machine learning algorithms have surfaced as a feasible solution, capable of discerning intricate patterns and relationships within the data. Nonetheless, the efficacy of these algorithms is significantly influenced by the precise tuning of hyperparameters, a task that can be quite complicated, particularly within IoT-enabled smart cities. This study concentrates on the creation of an innovative optimization framework that integrates the WildWood algorithm with a fractional-order variant of the Golden Search Algorithm for hyperparameter optimization. The proposed framework is measured through simulations in a smart city traffic management background, resulting in notable reductions in latency (up to 30%), energy consumption (up to 25%), and enhancements in throughput (up to 20%) when compared to conventional optimization techniques. Moreover, the optimized WildWood model achieves a Mean Squared Error (MSE) of 0.85, indicating its proficiency in accurately forecasting traffic flow patterns. The results underscore the effectiveness of the proposed framework in enhancing the reliability, efficiency, and sustainability of IoT-enabled smart city systems.