  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.
