  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.
