Traffic Shaping

Traffic shaping aims to optimize network traffic flow to achieve specific goals, such as mitigating congestion or prioritizing certain types of data. Current research focuses on developing sophisticated algorithms, including deep reinforcement learning and federated learning approaches, to manage traffic more effectively, particularly in complex scenarios like connected vehicle systems and 3D networks. These advancements are crucial for improving network performance, enhancing quality of service, and enabling new applications in areas like e-commerce and cybersecurity, where precise control over traffic distribution is essential.