Data Traffic Management in AI-IOt Network to Reduce Congestion

Abstract

As the artificial intelligence–internet of things (AI-IoT) network expands, the exponential growth in data generated by connected devices is leading to increased data traffic in AI-IoT networks. The surge in data traffic poses significant challenges to network infrastructure, causing congestion, latency, and inefficiencies. To address this issue, effective data traffic management techniques are crucial. This book chapter focuses on data traffic management in AI-AI-IoT networks to reduce congestion. It explores the underlying causes of congestion in AI-IoT networks and presents a comprehensive overview of existing congestion control mechanisms. Additionally, the chapter highlights the unique characteristics and requirements of AI-AI-IoT networks that differentiate them from traditional networks. It also examines various congestion detection and avoidance techniques designed explicitly for AI-AI-IoT environments, considering AI-IoT devices’ heterogeneity, scalability, and resource constraints. It discusses the importance of intelligent routing algorithms, traffic classification, and prioritization mechanisms in managing data traffic effectively. Moreover, the chapter delves into emerging technologies such as edge computing, fog computing, and network slicing, which can be leveraged to alleviate congestion in AI-IoT networks. It explores how off-loading computation and data processing tasks to the network edge can enhance traffic management and reduce latency. So this chapter provides valuable insights into the domain of data traffic management in AI-AI-IoT networks. It is a comprehensive resource for researchers, practitioners, and professionals interested in understanding and implementing effective congestion control mechanisms to ensure optimal performance and scalability in AI-IoT environments.