Key points are not available for this paper at this time.
Integrating machine learning (ML) with the Internet of Things (IoT) reveals hidden patterns and insights from extensive sensor data, enabling IoT to become omnipresent and make intelligent decisions without explicit programming. ML is essential for IoT to meet the future needs of businesses, governments, and individuals. IoT aims to sense its environment and automate decision-making through intelligent methods, emulating human decisions. This paper reviews and categorises existing literature on ML-enabled IoT from three perspectives: data, applications, and industries. We examine advanced methods and applications by reviewing various sources, emphasising how ML and IoT work together to create more innovative environments. We also discuss emerging trends such as the Internet of Behavior, pandemic management, autonomous vehicles, edge and fog computing, and lightweight deep learning. Furthermore, we identify challenges to IoT in four categories: technological, individual, business, and societal. This paper aims to leverage IoT opportunities and address challenges for a more prosperous and sustainable future.
Akintayo et al. (Sun,) studied this question.