[Paper Review] Fog Computing Approaches in Smart Cities: A State-of-the-Art Review

Maryam Songhorabadi, Morteza Rahimi+2 more|arXiv (Cornell University)|Nov 30, 2020

Smart Cities and Technologies92 references18 citations

TL;DR

This paper presents a systematic literature review (SLR) of fog computing approaches in smart cities, proposing a three-tier taxonomy—service-based, resource-based, and application-based—while analyzing evaluation factors, tools, algorithms, and challenges. It identifies key limitations of cloud-centric models and highlights fog computing's role in enhancing latency, security, and reliability for real-time urban applications.

ABSTRACT

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.

Motivation & Objective

• To address the limitations of cloud-centric computing in smart city applications requiring low latency, high security, and real-time responsiveness.
• To identify and classify existing fog computing approaches in smart cities through a systematic literature review (SLR).
• To propose a comprehensive taxonomy of fog computing approaches based on service, resource, and application dimensions.
• To evaluate the strengths, weaknesses, tools, and methods used in current fog computing research for smart cities.
• To identify open research issues and future trends in fog computing for smart city deployment.

Proposed method

• Conducted a systematic literature review (SLR) of 97 studies from peer-reviewed sources and arXiv, focusing on fog computing in smart cities.
• Proposed a three-class taxonomy: service-based (e.g., service composition, QoS), resource-based (e.g., resource allocation, scheduling), and application-based (e.g., healthcare, traffic management).
• Analyzed evaluation factors such as latency, energy efficiency, security, and reliability across reviewed studies.
• Categorized tools and simulation platforms used (e.g., NS-3, OMNeT++, CloudSim) and evaluated their suitability for fog simulation.
• Identified and classified algorithms used in each category, including heuristic, metaheuristic, and machine learning-based approaches.
• Synthesized open challenges and future research directions by sub-classifying practical and theoretical issues across the three taxonomy classes.

Experimental results

Research questions

• RQ1 What are the dominant application domains of fog computing in smart cities, and how do they influence system design?
• RQ2 How do existing fog computing approaches classify and categorize services, resources, and applications in smart city environments?
• RQ3 What evaluation metrics, tools, and simulation frameworks are most commonly used in fog computing research for smart cities?
• RQ4 What are the key advantages and limitations of fog computing compared to cloud and edge computing in smart city deployments?
• RQ5 What are the most critical open challenges and future research directions in fog computing for smart cities?

Key findings

• Fog computing significantly reduces latency and improves reliability for time-sensitive urban applications such as emergency response and real-time patient monitoring.
• The service-based category dominates research, with a focus on QoS-aware service composition and dynamic service discovery in smart city environments.
• Resource-based approaches emphasize efficient allocation and scheduling of computational and communication resources, often using heuristic or metaheuristic algorithms.
• Application-based studies show strong potential in healthcare, traffic control, and industrial automation, with real-time data processing being a key enabler.
• Simulation tools like NS-3 and CloudSim are widely used, but there remains a lack of standardized benchmarks and reproducible evaluation frameworks.
• Critical open challenges include security and privacy in decentralized fog nodes, interoperability across heterogeneous systems, and energy efficiency in large-scale deployments.