# All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

arXiv: https://arxiv.org/abs/1808.05283 · HTML: https://mlsanity.com/paper/1808.05283/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2018.

## Built on by (most cited first, 14 of 14)

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- [The Future of Consumer Edge-AI Computing](https://mlsanity.com/paper/2210.10514/) (2022)
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- [Intelligent Computing: The Latest Advances, Challenges and Future](https://mlsanity.com/paper/2211.11281/) (2022)
- [GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments](https://mlsanity.com/paper/2112.08916/) (2021)
- [Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks](https://mlsanity.com/paper/2202.10541/) (2022)
- [Unsupervised detection and open-set classification of fast-ramped flexibility activation events](https://mlsanity.com/paper/2111.02174/) (2021)
- [Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks](https://mlsanity.com/paper/2202.11098/) (2022)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [QECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing](https://mlsanity.com/paper/2311.02525/) (2023)
- [A Narrative Review of Identity, Data, and Location Privacy Techniques in Edge Computing and Mobile Crowdsourcing](https://mlsanity.com/paper/2401.11305/) (2024)
- [Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization](https://mlsanity.com/paper/2406.09478/) (2024)
- [Distributed Convolutional Neural Network Training on Mobile and Edge Clusters](https://mlsanity.com/paper/2409.09083/) (2024)

## Abstract (arXiv)

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.