Fog Computing: Helping the Internet of Things Realize Its Potential

By Amir Vahid Dastjerdi, Rajkumar Buyya. Computer, 2016

980 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/778b90dbd4edcb1df6201978c20aa860a26eab7c

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT (2017, background): “Because of its network edge computing feature, fog computing can not only provide low latency, location awareness but also improve real-time and quality of services in network-based applications [11]–[13].”
Edge-computing-driven Internet of Things: A Survey (2022, background): “Edge computing benefits from edge devices’ proximity to sensors while leveraging the on-demand scalability of cloud resources [42].”
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].”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, background): “IoT technologies can enable modernisations that improve life quality [1] and have the capability to collect, quantify and understand the surrounding environments.”
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications (2017, methodology): “Internet of Thing [21], [36], [79], [128], [129], [130], [131], [132], [133], [134], [135] • Healthcare • Wireless sensor systems • Smart grid • Smart Home • Smart City”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “Fog computing is a blend of cloud and edge computing where it is unknown to the user where in the network the data is stored, managed and processed, and the load is distributed automatically between remote servers and local resources [471], [471].”
Deep Reinforcement Learning for Cyber Security (2019, methodology): “For example, ICT has been advanced with the innovations of software-defined network (SDN) [1], [2], cognitive radio network (CRN) [3], [4], cloud computing [5], (mobile) edge caching [6], [7], and fog computing [8].”
A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges (2017, background): “Dastjerdi and Buyya [17] provide an overview of fog computing along with its characteristics.”
Survey on fog computing: architecture, key technologies, applications and open issues (2017, background): “• Fault tolerance: Fog computing should still provide services normally when there is a failure individual sensors, networks, service platforms, and applications (Dastjerdi and Buyya, 2016).”
Securing Fog Computing for Internet of Things Applications: Challenges and Solutions (2018, methodology): “Energy Consumption Collection [58]: In smart grid, the operation center frequently collects the power consumption of every household, which is used to optimize energy generation, distribution and billing.”
An Overview of Service Placement Problem in Fog and Edge Computing (2020, background): “Indeed diﬀerent works have been proposed to discuss the concept and the role of Fog computing [25, 38, 40, 117, 138, 146, 155, 158].”
Fog Computing and the Internet of Things: A Review (2018, background): “It also addresses issues regarding network connectivity and trafﬁc [50].”
Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions (2018, background): “[4], [95] stated that edge devices perform optimization by considering user behavior and network condition.”
A Survey on Internet of Things and Cloud Computing for Healthcare (2019, background): “recent technological evolution, such as fog computing and big data, extend cloud computing ability by supporting highly scalable computing platforms [59].”