Machine Learning-based Traffic Classification and Channel Allocation in IoT: A Survey

The increasing number of Internet of Things devices has resulted in the development of dynamic and heterogeneous traffic patterns. Efficient communication is required in order to maintain the smooth functioning of these networks. Traditional methods of channel allocation and traffic classification are not able to handle the complexity of these traffic patterns. Machine learning techniques are being widely used to address the various challenges related to the development and maintenance of IoT networks. They can help improve the efficiency of the networks by identifying and prioritizing the traffic patterns. This paper reviews the current state of the art in machine learning-based channel allocation and traffic classification techniques. The goal of this paper is to provide a comprehensive analysis of the current state-of-art techniques in the field of machine learning-based traffic classification and channel allocation. It also aims to highlight the potential future directions of this technology. The paper reviews the current state of the art in machine learning-based channel allocation and traffic classification techniques for the Internet of Things. The complexity of the traffic patterns encountered by the Internet of Things network is a major challenge that can be solved through the use of machine learning techniques. This paper presents an overview of the various techniques that are being used to improve the efficiency of IoT networks. The paper also reviews the various open research questions and the potential directions in the domain of machine learning in the context of IoT channel allocation and traffic classification. The objective of this survey paper is to provide practitioners and researchers with a convenient reference.