# Fog Computing Approaches in Smart Cities: A State-of-the-Art Review

arXiv: https://arxiv.org/abs/2011.14732 · HTML: https://mlsanity.com/paper/2011.14732/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2409.16444/) (2024)
- [PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Security](https://mlsanity.com/paper/2503.00331/) (2025)
- [Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security](https://mlsanity.com/paper/2410.01016/) (2024)
- [Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems](https://mlsanity.com/paper/2306.05567/) (2023)
- [Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach](https://mlsanity.com/paper/2408.12838/) (2024)
- [COVID-19 Probability Prediction Using Machine Learning: An Infectious Approach](https://mlsanity.com/paper/2408.12841/) (2024)

## Abstract (arXiv)

These days, the development of smart cities, specifically in location-aware, latency-sensitive, and security-crucial applications (such as emergency fire events, patient health monitoring, or real-time manufacturing) heavily depends on a more advance computing paradigms that can address these requirements. In this regard, fog computing, a robust cloud computing complement, plays a preponderant role by virtue of locating closer to the end-devices. Nonetheless, utilized approaches in smart cities are frequently cloud-based, which causes not only the security and time-sensitive services to suffer but also its flexibility and reliability to be restricted. So as to obviate the limitations of cloud and other related computing paradigms such as edge computing, this paper proposes a systematic literature review (SLR) for the state-of-the-art fog-based approaches in smart cities. Furthermore, according to the content of the reviewed researches, a taxonomy is proposed, falls into three classes, including service-based, resource-based, and application-based. This SLR also investigates the evaluation factors, used tools, evaluation methods, merits, and demerits of each class. Types of proposed algorithms in each class are mentioned as well. Above all else, by taking various perspectives into account, comprehensive and distinctive open issues and challenges are provided via classifying future trends and issues into practical sub-classes.