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Title: Fog Computing for Sustainable Smart Cities: A Survey
Authors: Perera, Charith
Qin, Yongrui
Estrella, Julio C.
Reiff-Marganiec, Stephan
Vasilakos, Athanasios V.
First Published: 29-Jun-2017
Publisher: Association for Computing Machinery (ACM)
Citation: ACM Computing Surveys, 2017, 50(3), article 32
Abstract: 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, specially 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 an sustainable IoT infrastructure for smart cities. In this paper, 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 towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities.
DOI Link: 10.1145/3057266
ISSN: 0360-0300
eISSN: 1557-7341
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2017, Association for Computing Machinery (ACM) . Deposited with reference to the publisher’s open access archiving policy.
Appears in Collections:Published Articles, Dept. of Computer Science

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