An Overview of Adaptive Network Control in the Internet of Things: Machine Learning Methods for Optimizing Traffic and Dynamic Resource Management

The Internet of Things (IoT) ecosystem has witnessed exponential growth, making network settings complex and dynamic. These challenges in energy efficiency, traffic, and resource allocation have already made adaptive network control an indispensable instrument for Internet of Things (IoT) systems. The survey provides a comprehensive overview of adaptive network control techniques, particularly those based on ML algorithms to maximize traffic handling and dynamic resource allocation. Focusing on Internet of Things (IoT) use cases such as edge computing, sensor networks, and heterogeneous device management, the research examines a wide variety of ML algorithms, including supervised, unsupervised, and reinforcement learning. The study shows that network scaling using ML-based methods will be stable and load-balanced and reduce latency continuously. Here adaptive resource management is explored with reinforcement learning (RL) methods that can alter on-demand storage, processing and bandwidth to match the dynamic requirements. It says federated learning provides a way to optimize decentralized systems without compromising user data while making full use of distributed resources. Recent studies from the wide spectrum of ML applications provide case studies and experimental findings for increasing fault tolerance, reducing energy consumption, and improving Quality of Service (QoS) in IoT networks. Enabling flexible, efficient, and sustainable IoT networks, this study presents the transformative potential of machine learning. Other important research issues identified include scalable algorithms, real-time decision-making abilities, and high security. Suggestions for future work reveals the area of hybrid ML models, 6G AI convergence and development of light weight algorithms that can be run in low power Internet of Things devices. This work aims to provide a foundation for researchers and practitioners to use machine learning for advanced network control in the context of IoT ecosystems.