Cloud and Fog-Based Traffic Control System for Social Internet of Vehicles

The Internet of Things (IoT) and the Internet of Vehicles (IoV) are crucial for optimizing resources and enhancing the efficiency of urban transportation and traffic management systems. As these technologies expand, real-time data from billions of connected devices is collected, processed, and analyzed, leading to better resource utilization, improved safety, and reduced traffic congestion. Implementing fog and cloud computing systems effectively reduces latency and optimizes resource consumption. This paper also explores the use of the Tiny CNN model to enhance performance through feature extraction, resulting in increased accuracy in traffic detection and management. With ongoing technological advancements, IoT and robotics are poised to create smarter, more efficient systems across various sectors, significantly impacting daily life and urban infrastructure. This paper proposes a Tiny CNN model for traffic management in cloud and fog environments. Compared to existing methods like AlexNet, the Tiny CNN model excels in input processing and performs optimally under diverse conditions, making it a strong candidate for intelligent traffic management systems.