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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Research works printed but not linked: 7
- 10.1016/0364-0213(90)90002-E · DOI · Paper page
- olah, 0, Understanding LSTM Networks
- 2016, State of the science fact sheet, National Oceanic and Atmospheric Administration (NOAA)
- tieleman, 2012, Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude, COURSERA Neural Netw Mach Learn, 4, 26
- goodfellow, 2016, Deep Learning, 1
- song, 0, DeepTransport: Prediction and simulation of human mobility and transportation mode at a citywide level, Proc IJCAI, 2618
- 2014, United Nations World Urbanization Prospects the 2014 revision highlights
Cited by
Sorted by citations
Atitallah, S. B., Driss, M., Boulila, W., & Ghézala, H. B. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100303 · Cited by 375
Deep learning for geological hazards analysis: Data, models, applications, and opportunities
Ma, Z., & Mei, G. (2021) · Earth-Science Reviews · doi:10.1016/j.earscirev.2021.103858 · Cited by 345
Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey
Chen, W., Qiu, X., Cai, T., Dai, H., Zheng, Z., & Zhang, Y. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3073036 · Cited by 334
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
Lei, L., Tan, Y., Zheng, K., Liu, S., Zhang, K., & Shen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988367 · Cited by 317
TinyML-Enabled Frugal Smart Objects: Challenges and Opportunities
Sanchez-Iborra, R., & Skarmeta, A. F. (2020) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2020.3005467 · Cited by 295
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
