All one needs to know about fog computing and related edge computing paradigms: A complete survey
Ashkan Yousefpour,Caleb Fung,Tam T. Nguyen,Krishna P. Kadiyala,Fatemeh Jalali,Amirreza Niakanlahiji,Jian Kong,Jason P. Jue +7 more
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Figures
Figure 8: Taxonomy of fog computing that is used in this survey. 
Figure 1: The structure of the survey. 
Figure 5: Fog brings several benefits for the application developers, applications, and different industries by distributing the core functions. 
Table 2: Attributes of fog-computing related paradigms 
Figure 2: Common cloud service models and their classifications relative what portion of the application stack is managed by cloud providers. 
Table 7: Summary of challenges and future research directions
Citations
Distributed Intelligence in Wireless Networks
Xiaolan Liu,Yuanwei Liu,Yue-lin Gao,Toktam Mahmoodi,Sangarapillai Lambotharan,Danny H. K. Tsang +5 more
TL;DR: A comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native-AI wireless networks is conducted, with a focus on the basic concepts ofnative- AI wireless networks, on the AIenabled edge computing, and the design of distributed learning architectures for heterogeneous networks.
12
Processing Complex Events in Fog-Based Internet of Things Systems for Smart Agriculture
TL;DR: In this article, the authors proposed a multi-tier complex event processing approach (sensor node, fog, and cloud) that promotes fast decision making and is based on information with 98% accuracy.
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•Posted Content
A Discussion on Context-awareness to BetterSupport the IoT Cloud/Edge Continuum
TL;DR: The paper provides guidelines for future research in the context of Cloud-Edge and the application of context-awareness to assist in a higher degree of automation of the network and, as consequence, a better support of the Cloud to Edge continuum.
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An ICN-based Approach for Service Caching in Edge/Fog Environments
Hamza Ben-Ammar,Yacine Ghamri-Doudane +1 more
- 01 Dec 2020
TL;DR: In this article, the authors propose a novel service caching strategy, called 3Q, that is the first to argue on the caching of both, the service instances, consuming computing resources, as well as their associated source codes, consuming storage resources.
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Computation Offloading for Machine Learning in Industrial Environments
Mian Guo,Mithun Mukherjee,Gen Liang,Jinyou Zhang +3 more
- 18 Oct 2020
TL;DR: A machine learning-based offloading problem for edge computing based machine learning in an industrial environment is formulated with the goal of minimizing the training delay, and an energy-constrained delay-greedy algorithm is designed to solve the problem.
12
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