Machine Learning Algorithms for Internet of Things and Smart Intelligent Decisions

The collaboration of machine learning (ML) with the Internet of Things (IoT) is making a drastic change in IoT networks, including network management, traffic engineering, and security, and providing quality services. The recent surveys were based on ML techniques applied to IoT, focusing on areas like device identification, IoT application efficiency, edge computing, and resource optimization. Here we studied the potential of distributed machine learning (DML), including federated learning (FL) and multi-agent reinforcement learning (RL) to enhance the IoT system for making smart will discuss decisions. In addition, we will discuss the role of deep learning (DL) in analyzing IoT big data and streaming data which will be helpful for decision-making. We will learn various ML algorithms and applications. The integration of IoT with ML will also be discussed with its applications for smart decision-making. Furthermore, we will also study the challenges such as model selection, data concept drift, and frequent updates. Moreover, emerging trends like the internet of behavior (IOB), autonomous vehicle, and societal impact of IoT and ML are discussed. In this chapter, the main focus is on open issues and future direction for the integration of ML with IoT to enable smarter environments and move efficient IoT applications.