IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective

TL;DR

This paper reviews AutoML techniques for IoT data analytics, addressing challenges like model selection, tuning, and concept drift, and demonstrates their application in IoT anomaly detection.

Contribution

It provides a comprehensive review of AutoML methods tailored for IoT data analysis and presents a case study on anomaly detection to illustrate practical implementation.

Findings

AutoML can effectively automate model selection and tuning for IoT data.

Applying AutoML improves anomaly detection performance in IoT systems.

The paper identifies key challenges and future research directions in AutoML for IoT.

Abstract

With the wide spread of sensors and smart devices in recent years, the data generation speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems, massive volumes of data must be processed, transformed, and analyzed on a frequent basis to enable various IoT services and functionalities. Machine Learning (ML) approaches have shown their capacity for IoT data analytics. However, applying ML models to IoT data analytics tasks still faces many difficulties and challenges, specifically, effective model selection, design/tuning, and updating, which have brought massive demand for experienced data scientists. Additionally, the dynamic nature of IoT data may introduce concept drift issues, causing model performance degradation. To reduce human efforts, Automated Machine Learning (AutoML) has become a popular field that aims to automatically select, construct, tune, and update machine learning models to achieve the best performance on specified tasks. In this paper, we conduct a review of existing methods in the model selection, tuning, and updating procedures in the area of AutoML in order to identify and summarize the optimal solutions for every step of applying ML algorithms to IoT data analytics. To justify our findings and help industrial users and researchers better implement AutoML approaches, a case study of applying AutoML to IoT anomaly detection problems is conducted in this work. Lastly, we discuss and classify the challenges and research directions for this domain.