The DOI matches its Crossref record; 147 of the 155 references it cites were found in a registry or an index.
A Survey on an Emerging Area: Deep Learning for Smart City Data
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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“The size of memory and power capacity in edge devices is limited, since these devices have small memory and short energy lifetimes.”
From TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications (2022) · cites this paper as [12]
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“Power and memory consumption are present challenges; heavy-weight DL models consume memory and a lot of power.”
From TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications (2022) · cites this paper as [12]
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Metadata
- 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)
- Venue
- IEEE Transactions on Emerging Topics in Computational Intelligence
- Published
- 2019-10
- Volume
- 3
- Issue
- 5
- Pages
- 392-410
- ISSN
- 2471-285X (ISSN-L)
- 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
- 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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