Machine Learning and Internet of Things

The combination of machine learning (ML) and artificial intelligence (AI) with Internet of Things (IoT) systems has transformed numerous industries through intelligent decision-making and automation. This chapter introduces integration methods and applications of ML and AI in IoT environments. The convergence of ML and the IoT has revolutionized decision-making systems, but challenges such as data management, computational complexity, and privacy remain. Integration methods like edge computing and federated learning (FL) are being explored. This chapter presents the synergy between ML and IoT with a focus on how advanced algorithms enable real-time data processing, predictive analysis, and self-driving decisions in the IoT ecosystem. The chapter also discusses the increase in the robustness of IoT applications due to transfer learning and ensemble methods. With ML algorithms, IoT networks are transforming into self-learning, autonomous networks capable of making intelligent decisions under dynamic conditions, paving the way for a brighter and more connected future. This chapter explores the application of ML to IoT networks with a specific focus on supervised, unsupervised, deep, and reinforcement learning (RL) algorithms. It explains data preprocessing, feature engineering, time-series analysis, and optimization methods. The chapter discusses privacy, security, ethical issues, and bias in ML-based IoT decision-making.