The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
TinyML Meets IoT: A Comprehensive Survey
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Citations
Cites
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
The NIST definition of cloud computing
Mell, P. M., & Grance, T. (2011) · doi:10.6028/nist.sp.800-145 · Cited by 11,795
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733
A Survey on Mobile Edge Computing: The Communication Perspective
Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2745201 · Cited by 5,607
Han, S., Mao, H., & Dally, W. J. (2015) · arXiv · doi:10.48550/arxiv.1510.00149 · Cited by 4,662
Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
Next generation 5G wireless networks: a comprehensive survey
Agiwal, M., Roy, A., & Saxena, N. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2532458 · Cited by 3,444
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Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2683200 · Cited by 2,793
Advances in Neural Information Processing Systems 37
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Research works printed but not linked: 6
- Doyu, 2020, Bringing Machine Learning to the Deepest IoT Edge with TinyML as-a-Service, IEEE IoT Newsletter, 11
- MacGillivray, 2019, Internet of Things: Spending trends and outlook, Tech. Rep.
- Hu, 2015, Mobile edge computing—A key technology towards 5G, ETSI white paper, 11, 1
- Dutta, 2017, A novel low cost hand-held tea flavor estimation system, IEEE Trans. Ind. Electron., 65, 4983, 10.1109/TIE.2017.2772184 · doi:10.1109/tie.2017.2772184
- Guimarães, C.J. and Fernandes, M.A., “Real-time Neural Networks Implementation Proposal for Microcontrollers,” Jun. 2020. [Online]. Available: https://arxiv.org/pdf/2006.05344.pdf. · arXiv · doi:10.3390/electronics9101597
- Garofalo, 2019, PULP-NN: accelerating quantized neural networks on parallel ultra-low-power RISC-V processors, Philosophical Transactions of the Royal Society A, 378, 2020
Cited by
A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366
“They also reported the same remaining challenges including limitation of existing benchmarks, hardware limitations when it comes to large amount of data, software and hardware co-design which is specifically addressed in [25].”
BackgroundMachine Learning for Microcontroller-Class Hardware: A Review
Saha, S. S., Sandha, S. S., & Srivastava, M. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3210773 · Cited by 253
A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions
Andrade, P., Silva, I., Silva, M., Flores, T., Cassiano, J., & Costa, D. G. (2022) · Sensors · doi:10.3390/s22103838 · Cited by 66
Deep learning for asteroids autonomous terrain relative navigation
Mancini, P., Cannici, M., & Matteucci, M. (2023) · Advances in Space Research · doi:10.1016/j.asr.2022.04.020 · Cited by 10
Towards lightweight deep neural network for smart agriculture on embedded systems
Du, P., Polonelli, T., Magno, M., & Cheng, Z. (2022) · 2022 IEEE Sensors Applications Symposium (SAS) · doi:10.1109/sas54819.2022.9881382 · Cited by 5
Metadata
- Authors
- Dutta, D. L., & Bharali, S.
- Venue
- Internet of Things
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 16
- Pages
- 100461
- ISSN
- 2542-6605 (ISSN-L)
- OpenAlex
- W3202905974
- Cited by
- 328
- References
- 76
- FWCI
- 20.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
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- Dutta, D. L., & Bharali, S. (2021). TinyML Meets IoT: A Comprehensive Survey. Internet of Things. https://doi.org/10.1016/j.iot.2021.100461
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
