Fog Computing for Sustainable Smart Cities: A Survey
Open accessAbstract
The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build a sustainable IoT infrastructure for smart cities. In this article, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them, to shed light on future research directions on realizing Fog computing for building sustainable smart cities.
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Environmentally sustainable smart cities roughly started to emerge from 2017 onwards.
“This roughly started from 2017 onwards”
From Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review (2023) · cites this paper as Perera et al. 2017
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- Authors
- Perera, C., Qin, Y., Estrella, J. C., Reiff-Marganiec, S., & Vasilakos, A. V.
- Affiliations
- The Open University (GB)
- University of Huddersfield (GB)
- University of Sao Paulo
- University of Leicester
- Luleå University of Technology (SE)
- Venue
- ACM Computing Surveys
- Published
- 2017-06-29
- Volume
- 50
- Issue
- 3
- Pages
- 1-43
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2604442880
- Cited by
- 459
- References
- 121
- Citations by year
- 2026: 14 · 2025: 32 · 2024: 37 · 2023: 46 · 2022: 39 · 2021: 71 · 2020: 78 · 2019: 76 · 2018: 55 · 2017: 11
- FWCI
- 40.08
- Type
- journal article
- Language
- en
- Open access
- green
- dl.acm.org
- Locations
- ACM Computing Surveys (publishedVersion)
- Leicester Research Archive (University of Leicester) (submittedVersion, other-oa)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Huddersfield Research Portal (University of Huddersfield) (submittedVersion, other-oa)
- Funders
- European Research Council Advanced (291652 (ASAP))
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- fog computing, edge computing, smart cities, Internet of Things, cloud computing, resource waste, data analytics, open problems
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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