Fog Computing Based on Machine Learning: A Review

Abstract

Internet of Things (IoT) systems usually produce massive amounts of data, while the number of devices connected to the internet might reach billions by now. Sending all this data over the internet will overhead the cloud and consume bandwidth. Fog Computing's (FC) promising technology can solve the issue of computing and networking bottlenecks in large-scale IoT applications. This technology complements cloud computing by providing processing power and storage to the edge of the network. However, it still suffers from performance and security issues. Thus, machine learning (ML) attracts attention for enabling FC to settle its issues. Lately, utilizing ML has been a growing trend to improve FC applications, like resource management, security, lessen latency, and power usage. Also, intelligent FC was studied to address industry 4.0, bioinformatics, blockchain, and vehicular communication system issues. Due to the ML vital role in the FC paradigm, this work will shed light on recent studies that utilized ML in an FC environment. Background knowledge about ML and FC was also presented. This paper categorized the surveyed studies into three groups according to the aim of ML implementation. These studies were thoroughly reviewed and compared using sum-up tables. The results showed that not all studies used the same performance metric except those worked on security issues. In conclusion, the proposed ML models' simulations are not sufficient due to the heterogeneous nature of the FC paradigm.