Machine learning and data analytics for the IoT

TL;DR

This paper reviews how machine learning is applied to IoT data, identifies current challenges, proposes a framework for adaptive learning among IoT applications, and discusses future directions for intelligent IoT systems.

Contribution

It introduces a novel framework enabling IoT applications to learn adaptively from each other, addressing existing barriers in data processing and communication protocols.

Findings

Identified key challenges in IoT data processing for ML

Proposed a framework for adaptive IoT learning

Demonstrated the framework through a real case study

Abstract

The Internet of Things (IoT) applications have grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e., cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data are processed for machine learning analysis and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.