Machine Learning Techniques for Enhancing Internet of Things (IoT) Performance A Review

Abstract

The Internet of Things (IoT) is basically billions of interconnected smart devices that can communicate with little interference from humans, thus making life easier. The IoT is a fast-moving area of research, and the challenges are growing, thus requiring continuous improvement. As IoT systems become more challenging to improve, machine learning (ML) is increasingly incorporated into IoT systems to develop better capabilities. This article review explores several machine learning techniques aimed at enhancing the performance of IoT systems. It highlights the growing importance of integrating machine learning with IoT to address challenges such as data management, security, and real-time processing. The techniques discussed include supervised learning, unsupervised learning, reinforcement learning, deep learning, ensemble methods, anomaly detection, and federated learning. Each method is evaluated for its effectiveness in optimizing IoT applications, such as predictive maintenance, energy efficiency, and smart city solutions. The review emphasizes the potential of these techniques to improve decision-making processes, automate operations, and enhance user experiences. Additionally, it addresses the limitations and challenges associated with implementing machine learning in IoT environments, including data privacy concerns and the need for robust algorithms capable of handling diverse datasets. Overall, the article underscores the transformative role of machine learning in advancing IoT capabilities and suggests future research directions to further leverage these technologies for improved system performance and reliability.