Fog and IoT: An Overview of Research Opportunities

By Mung Chiang, Tao Zhang. 2016

2,138 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Adaptive Federated Learning in Resource Constrained Edge Computing Systems (2018, background): “It has been predicted that the data generation rate will exceed the capacity of today’s Internet in the near future [2].”
Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning (2019, background): “It is prohibitive to transmit these datasets to the clouds for centralized processing and also a burden on backbone networks [1].”
Accelerating the Convergence Rates of Distributed Subgradient Methods with Adaptive Quantization (2018, background): “information processing in edge (fog) computing, which has recently received a surge of interests due to the emergence of the Internet of Thinfs [2].”
Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges (2018, background): “There are related survey papers [20], [37]–[40] that”
A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges (2017, background): “Several surveys have also been published on fog computing at large [4], [19], [20] and also in the context of speciﬁc application domains, i.e., vehicular Ad-hoc NETworks (VANETs) [21], Radio Access Networks (RAN) [22], [23], and Internet of Things [24].”
Collaborative Cloud and Edge Computing for Latency Minimization (2019, background): “TO COPE with the tremendous growth of mobile data traffic, edge computing has been recently proposed as a promising means to substantially improve the computation performance by deploying cloud computing services at the edge of a network [2]– [4].”
Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching (2017, background): “In [17], the authors overview the opportunities and challenges of fog, especially the applic ations of fog computing in IoT.”
A Fundamental Tradeoff Between Computation and Communication in Distributed Computing (2016, background): “, [52], [53]), abundant computation resources scattered across the network edge (e.”
Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities (2019, background): “Collaboration within the fog cloud of IoT devices was discussed in [33].”
Security and Privacy in Fog Computing: Challenges (2017, background): “[12] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, ‘‘Edge computing: Vision and challenges,’’ IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, Oct. 2016.”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “In [437], the authors summarize the opportunities and challenges of fog computing in the networking context of IoT.”
A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT (2018, methodology): “For future generation computing, fog computing is an evolving framework that combines cloud computing and IoT [6].”
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks (2018, background): “Meanwhile, the device computing power can be effectively enhanced by the recent development of mobile-edge computing (MEC) technology [2], [3].”
Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading (2017, background): “OVER the past few years, the explosive popularity of mobile devices, such as smart-phones, tablets, and wearable devices, has been accelerating the development of the Internet of Things (IoT) [1], [2].”
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications (2017, background): “Compared to Cloud, Fog has advantages in three dimensions: exploiting storage, computing and control functions, communication and networking at or near the end user [42].”