From Cloud Down to Things: An Overview of Machine Learning in Internet of Things

TLDR

The proliferation of IoT devices has outpaced cloud‑centric data processing, prompting a shift to edge computing and the need to extend machine learning across the cloud‑to‑things continuum. This review examines the role of machine learning in IoT from cloud to embedded devices, highlighting challenges and research trends for efficient edge deployment. The authors systematically retrieve, classify, and categorize ML applications in IoT by domain, data type, technique, and position along the cloud‑to‑things spectrum. The study identifies emerging topics and application domains where machine learning is increasingly applied within the IoT ecosystem.

Abstract

With the numerous Internet of Things (IoT) devices, the cloud-centric data processing fails to meet the requirement of all IoT applications. The limited computation and communication capacity of the cloud necessitate the edge computing, i.e., starting the IoT data processing at the edge and transforming the connected devices to intelligent devices. Machine learning (ML) the key means for information inference, should extend to the cloud-to-things continuum too. This paper reviews the role of ML in IoT from the cloud down to embedded devices. Different usages of ML for application data processing and management tasks are studied. The state-of-the-art usages of ML in IoT are categorized according to their application domain, input data type, exploited ML techniques, and where they belong in the cloud-to-things continuum. The challenges and research trends toward efficient ML on the IoT edge are discussed. Moreover, the publications on the “ML in IoT” are retrieved and analyzed systematically using ML classification techniques. Then, the growing topics and application domains are identified.