Intelligent edge computing: state-of-the-art techniques and applications

Abstract

To enable intelligent decisions at the network edge, supervised and unsupervised machine learning techniques and their variations are highly utilized in recent research studies. These include techniques and the corresponding applications such as detecting manufacturing faults in a smart factory setting, monitoring patient activities and health problems in smart health systems, detecting security attacks on the Internet of Things devices, and finding the rare events in the audio signals. In this paper, we present an extensive review of state-of-the-art techniques and applications of intelligent edge computing and provide classification and discussion of various approaches in this field.