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

By Ashkan Yousefpour, Caleb Fung, Tam Nguyen, Krishna P. Kadiyala, Fatemeh Jalali, Amirreza Niakanlahiji, Jian Kong, Jason P. Jue. Journal of systems architecture, 2018

1,215 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3e4388586eb2538657a24ca1ace3c6877ec2235b

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Citations in the corpus

Federated Learning in Edge Computing: A Systematic Survey (2022, background): “…[25] Survey on convergence of the intelligent edge and edge intelligence X X [62] Communication and networking issues being addressed by DRL X X X [33] Survey on fog computing and related edge computing paradigms X X X X [6] Survey on the integrated management of mobile computing and wireless…”
Resource Management in Fog/Edge Computing (2018, methodology): “A model that makes use of both edge resources and the cloud is referred to as “fog computing” [20, 42, 196].”
IoT in Smart Cities: A Survey of Technologies, Practices and Challenges (2021, background): “Another concept about Edge computing as discussed in [38] deﬁnes the Edge computing layer as an intermediary layer between the Fog and the ‘things’ (sensors) rather than edge nodes.”
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, background): “More detailed information can be found in [8], [22], [23].”
An Overview of Service Placement Problem in Fog and Edge Computing (2020, background): “[24], which provides a tutorial on Fog Computing, compares the Fog to the related computing paradigms, and discusses the resource management and service deployment (orchestration and migration) in Fog environment.”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “us reducing the execution latency and network congestion [11], [12]. Since fog nodes are generally deployed in distributed locations, they are widespread and geographically available in large numbers [13]. Due to its characteristics, fog computing plays an important role in many use cases and applic”
EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications (2019, background): “The most part of the numerical results and performance comparisons available in the open literature refers to three-tier Mobile-Fog-Cloud offloading systems, in which single Fog nodes are involved (see, for example, [11] and references therein).”