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Fog computing and its role in the internet of things
Open accessAbstract
Fog Computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are: a) Low latency and location awareness; b) Wide-spread geographical distribution; c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Heterogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
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“Fog computing can be used as a solution to the shortcomings of the cloud for smart transportation systems.”
From Smart Transportation: An Overview of Technologies and Applications (2023) · cites this paper as [23]
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- F. Bonomi . Connected vehicles, the internet of things, and fog computing . VANET 2011 , 2011 . F. Bonomi. Connected vehicles, the internet of things, and fog computing. VANET 2011, 2011.
- 10.1145/161468.161471 · DOI
- 10.5555/944919.944974 · DOI
- 10.1145/332040.332491 · DOI
- M. J. Sannella . Constraint satisfaction and debugging for interactive user interfaces . 2003 . M. J. Sannella. Constraint satisfaction and debugging for interactive user interfaces. 2003.
- P. Tavel . Modeling and simulation design . 2007 . P. Tavel. Modeling and simulation design. 2007.
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Metadata
- Authors
- Bonomi, F., Milito, R., Zhu, J., & Addepalli, S.
- Affiliations
- Cisco Systems (United States) (US)
- ORCID
- Zhu, Jiang
- Venue
- Proceedings of the first edition of the MCC workshop on Mobile cloud computing
- Publisher
- ACM
- Published
- 2012-08-17
- Pages
- 13-16
- OpenAlex
- W2114623221
- Cited by
- 6,244
- References
- 0
- Citations by year
- 2026: 198 · 2025: 266 · 2024: 327 · 2023: 426 · 2022: 508 · 2021: 632 · 2020: 778 · 2019: 916 · 2018: 931 · 2017: 640 · 2016: 357 · 2015: 183 · 2014: 60 · 2013: 20 · 2012: 2
- FWCI
- 71.11
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- fog computing, Internet of Things, edge computing, low latency, location awareness, wireless sensor and actuator networks, connected vehicles, smart grid, smart cities, real-time applications, heterogeneous networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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