Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification

TL;DR

This paper evaluates and compares the performance of 11 machine and deep learning algorithms on IoT data classification tasks across six datasets, highlighting the superior performance of Random Forests and certain deep learning models.

Contribution

It provides a comprehensive performance comparison of multiple AI algorithms for IoT data classification, including convergence speed analysis.

Findings

Random Forests outperform other machine learning models.

ANN and CNN achieve notable results among deep learning models.

Performance metrics vary across algorithms and datasets.

Abstract

In recent years, the growth of Internet of Things (IoT) as an emerging technology has been unbelievable. The number of networkenabled devices in IoT domains is increasing dramatically, leading to the massive production of electronic data. These data contain valuable information which can be used in various areas, such as science, industry, business and even social life. To extract and analyze this information and make IoT systems smart, the only choice is entering artificial intelligence (AI) world and leveraging the power of machine learning and deep learning techniques. This paper evaluates the performance of 11 popular machine and deep learning algorithms for classification task using six IoT-related datasets. These algorithms are compared according to several performance evaluation metrics including precision, recall, f1-score, accuracy, execution time, ROC-AUC score and confusion matrix. A specific experiment is also conducted to assess the convergence speed of developed models. The comprehensive experiments indicated that, considering all performance metrics, Random Forests performed better than other machine learning models, while among deep learning models, ANN and CNN achieved more interesting results.