Fog Computing Based on Machine Learning: A Review




Fog Computing, Machine Learning, IoT, Optimization


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 the 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, there has been a growing trend in utilizing ML to improve FC applications, like resource management, security, lessen latency and power usage. Also, intelligent FC was studied to address issues in industry 4.0, bioinformatics, blockchain and vehicular communication system. Due to the ML vital role in the FC paradigm, this work will shed light on recent studies utilized ML in a FC environment. Background knowledge about ML and FC 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 simulations of proposed ML models are not sufficient due to the heterogeneous nature of the FC paradigm.

Author Biographies

Fady Esmat Fathel Samann, Duhok Polytechnic University, Kurdistan Region, Duhok, Iraq

Fady E. F. Samann holds an M.Sc. degree in Electronic Communications and Computer Engineering from the University of Nottingham-UK. Currently, he is studying for a Ph.D. in Information and Communications Technologies at the Duhok Polytechnic University, Kurdistan Region, Duhok, Iraq. He published articles in multiple fields.

Adnan Mohsin Abdulazeez, Duhok Polytechnic University, Kurdistan Region, Duhok, Iraq

Adnan Mohsin Abdulazeez received the B.Sc. degree in electrical and electronic engineering, the M.Sc. degree in computer and control engineering, and the Ph.D. degree in computer engineering. He is currently the President of Duhok Polytechnic University and a Professor of computer engineering and science. He has been awarded the title of a Professor since 2013. He is keen to carry out his administrative and academic responsibilities simultaneously. He supervised and still supervises a large number of master and doctoral students. Besides, he focuses his attention on publishing scientific researches in esteemed international scientific journals.

Shavan Askar, Erbil Polytechnic University, College of Engineering, Kurdistan Region, Erbil, Iraq

Shavan Askar (Associate Professor of Computer Networks) is currently a faculty member at the College of Engineering\EPU. He received his Ph.D. degree in Electronic Systems Engineering from the University of Essex\UK in 2012. He obtained his MSc (2003) and BSc (2001) degrees from the Control and Systems Engineering Dept. of the University of Baghdad. His interest fields include IoT, SDN, Optical Networks, and 5G. While he was in the UK, he worked as a Researcher on two European projects; the MAINS project and the ADDONAS project.




How to Cite

Samann, F. E. F., Abdulazeez, A. M., & Askar, S. (2021). Fog Computing Based on Machine Learning: A Review. International Journal of Interactive Mobile Technologies (iJIM), 15(12), pp. 21–46.