# Fog Computing: A Taxonomy, Survey and Future Directions

arXiv: https://arxiv.org/abs/1611.05539 · HTML: https://mlsanity.com/paper/1611.05539/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2016.

## Built on by (most cited first, 4 of 4)

- [A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments](https://mlsanity.com/paper/2110.12415/) (2021)
- [A multiagent based framework secured with layered SVM-based IDS for remote healthcare systems](https://mlsanity.com/paper/2104.06498/) (2021)
- [Security Challenges for Cloud or Fog Computing-Based AI Applications](https://mlsanity.com/paper/2310.19459/) (2023)
- [Energy and Thermal-aware Resource Management of Cloud Data Centres: A Taxonomy and Future Directions](https://mlsanity.com/paper/2107.02342/) (2021)

## Abstract (arXiv)

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.