Cited by 167Smart Cities and TechnologiesAI and Big Data ApplicationsInternet of Things and AI

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A Survey on an Emerging Area: Deep Learning for Smart City Data

Chen, Qi, Wang, Wei, Wu, Fangyu, De, Suparna, Wang, Ruili, Zhang, Bailing, Huang, Xin·2019·IEEE Transactions on Emerging Topics in Computational Intelligence

DOI 10.1109/tetci.2019.2907718

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Abstract

Rapid urbanization has brought about great challenges to our daily lives, such as traffic congestion, environmental pollution, energy consumption, public safety, and so on. Research on smart cities aims to address these issues with various technologies developed for the Internet of Things. Very recently, the research focus has shifted toward processing of massive amount of data continuously generated within a city environment, e.g., physical and participatory sensing data on traffic flow, air quality, and health care. Techniques from computational intelligence have been applied to process and analyze such data, and to extract useful knowledge that helps citizens better understand their surroundings and informs city authorities to provide better and more efficient public services. Deep learning, as a relatively new paradigm in computational intelligence, has attracted substantial attention of the research community and demonstrated greater potential over traditional techniques. This paper provides a survey of the latest research on the convergence of deep learning and smart city from two perspectives: while the technique-oriented review pays attention to the popular and extended deep learning models, the application-oriented review emphasises the representative application domains in smart cities. Our study showed that there are still many challenges ahead for this emerging area owing to the complex nature of deep learning and wide coverage of smart city applications. We pointed out a number of future directions related to deep learning efficiency, emergent deep learning paradigms, knowledge fusion and privacy preservation, and hope these would move the relevant research one step further in creating truly distributed intelligence for smart cities.

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Authors: Chen, Q., Wang, W., Wu, F., De, S., Wang, R., Zhang, B., & Huang, X.
Affiliations: Xi’an Jiaotong-Liverpool University (CN)
University of Surrey (GB)
Massey University (NZ)
Soochow University (CN)
ORCID: Chen, Qi
Wang, Wei
Wu, Fangyu
De, Suparna
Wang, Ruili
Zhang, Bailing
Huang, Xin
Venue: IEEE Transactions on Emerging Topics in Computational Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019-10
Volume: 3
Issue: 5
Pages: 392-410
ISSN: 2471-285X (ISSN-L)
DOI: 10.1109/tetci.2019.2907718
OpenAlex: W2943201207
Cited by: 167
References: 155
Citations by year: 2026: 17 · 2025: 18 · 2024: 19 · 2023: 35 · 2022: 27 · 2021: 34 · 2020: 16 · 2019: 1
FWCI: 13.05
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Emerging Topics in Computational Intelligence (publishedVersion)
Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
Funders: Xi’an Jiaotong-Liverpool University (RDF-16-01-34)
Jiangsu Planned Projects for Postdoctoral Research Funds (2018K005A)
National Natural Science Foundation of China (61701418)
Innovation Projects of The Next Generation Internet Technology (NGII20170301)
Topics: Social Sciences › Urban Studies › Smart Cities and Technologies
Physical Sciences › Computer Science › Information Systems › AI and Big Data Applications
Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords: deep learning, smart cities, Internet of Things, urban sensing, participatory sensing, traffic flow prediction, air quality monitoring, health data, knowledge fusion, privacy preservation, distributed intelligence, urban public services

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A Survey on an Emerging Area: Deep Learning for Smart City Data

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