A Comprehensive Survey on Fog Computing: State-of-the-art and Research Challenges
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Abstract (arXiv)
Cloud computing with its three key facets (i.e., IaaS, PaaS, and SaaS) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service Level Agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This article presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as Tactile Internet.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Oct 2017.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
7 papers.
- 2021Compacting Deep Neural Networks for Internet of Things: Methods and Applicationscited by 6
- 2021EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehiclescited by 3
- 2022Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligencecited by 2
- 2022Distributed Task Management in Fog Computing: A Socially Concave Bandit Gamecited by 1
- 2021Learning-based decentralized offloading decision making in an adversarial environmentcited by 0
- 2022A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networkscited by 0
- 2026Downsides of Smartness Across Edge-Cloud Continuum in Modern Industrycited by 0
- 2026Downsides of Smartness Across Edge-Cloud Continuum in Modern Industry31 Mar 2026
- 2022A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks23 Jun 2022
- 2022Distributed Task Management in Fog Computing: A Socially Concave Bandit Game28 Mar 2022
- 2022Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence27 Jan 2022
- 2021Learning-based decentralized offloading decision making in an adversarial environment26 Apr 2021
- 2021Compacting Deep Neural Networks for Internet of Things: Methods and Applications20 Mar 2021
- 2021EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles15 Jan 2021