Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review

TL;DR

This review discusses how machine learning-based pattern recognition techniques are applied in IoT devices, highlighting their benefits, challenges, and the most effective algorithms like SVM, KNN, and Random Forest.

Contribution

It provides a comprehensive overview of machine learning algorithms used for pattern recognition in IoT, identifying the most effective methods and current research trends.

Findings

Support vector machine, k-nearest neighbor, and random forest are the most effective algorithms.

Pattern recognition improves IoT device performance in accuracy and efficiency.

Research varies case by case, but general trends favor certain algorithms.

Abstract

The Internet of things (IoT) is a rapidly advancing area of technology that has quickly become more widespread in recent years. With greater numbers of everyday objects being connected to the Internet, many different innovations have been presented to make our everyday lives more straightforward. Pattern recognition is extremely prevalent in IoT devices because of the many applications and benefits that can come from it. A multitude of studies has been conducted with the intention of improving speed and accuracy, decreasing complexity, and reducing the overall required processing power of pattern recognition algorithms in IoT devices. After reviewing the applications of different machine learning algorithms, results vary from case to case, but a general conclusion can be drawn that the optimal machine learning-based pattern recognition algorithms to be used with IoT devices are support vector machine, k-nearest neighbor, and random forest.