AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

Dr Sukhpal Singh Gill
2 min readFeb 7



•Review AI/ML approaches used for the realization of AI/ML for fog/edge computing.

•Discuss the background of AI/ML for fog/edge computing with broad taxonomy.

•Present a current status of AI/ML-based resource management in fog/edge computing.

•Propose a taxonomy of AI/ML-based resource management methods in fog/edge computing.

•Identify open issues and future directions for the union of edge and AI as Edge AI.


Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing.

Fig. 1. The organization of this Systematic Literature Review (SLR).


Sundas Iftikhar, Sukhpal Singh Gill, Chenghao Song, Minxian Xu, Mohammad Sadegh Aslanpour, Adel N. Toosi, Junhui Du, Huaming Wu, Shreya Ghosh, Deepraj Chowdhury, Muhammed Golec, Mohit Kumar, Ahmed M. Abdelmoniem, Felix Cuadrado, Blesson Varghese, Omer Rana, Schahram Dustdar, Steve Uhlig, AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions, Internet of Things, Elsevier, Volume 21, 2023, 100674. arXiv




Dr Sukhpal Singh Gill

Dr. Gill is Assistant Professor in Queen Mary University of London. He is Associate Editor in Elsevier IoT, Wiley ETT & IET Networks. W: