IEEE Access Special Section Editorial: Data Mining for Internet of Things

Abstract

It is an irrefutable fact that the Internet of Things (IoT) will eventually change our daily lives because its applications and relevant technologies have been or will be penetrating our daily lives. Also, the IoT is aimed to connect all the things (e.g., devices and systems) together via the Internet, thus making it easy to collect the data of users or environments and to find out useful information from the gathered data by using data mining technologies. As a consequence, how intelligent systems are developed for the IoT has become a critical research topic today. This means that artificial intelligence (AI) technologies (e.g., supervised learning, unsupervised learning, and semi-supervised learning) were used in the development of intelligent systems for analyzing the data captured from IoT devices or making decisions for IoT systems. It can be easily seen that AI can make an IoT system more intelligent and thus more accurate. For example, various sensors can be used for a smart home system to pinpoint the location and analyze the behavior of a human; however, with AI technologies, a more accurate prediction can be provided on the two pieces of information of a human. One of the most important uses for AI technologies is to make IoT systems more intelligent in order to provide a more convenient environment for users; thus, how to use existing AI technologies or develop new AI technologies to construct a better IoT system has attracted the attention of researchers from different disciplines in recent years. That is why, besides using existing supervised, unsupervised, semi-supervised learning algorithms, data mining algorithms, and machine learning algorithms, several recent studies have also attempted to develop new intelligent methods for the devices or systems for the IoT. All these approaches for making an IoT system more intelligent can also be found in the articles of this Special Section.