A Comprehensive Review of Optimisation Techniques in Machine Learning for Edge Devices-Reference-Cited by-同舟云学术

A Comprehensive Review of Optimisation Techniques in Machine Learning for Edge Devices

Published:2022 Issue: Volume: Page:555-572
ISSN:2367-4512
Container-title:Mobile Computing and Sustainable Informatics
language:
Short-container-title:

Author:

Alwin Infant P.,Renjith P. N.,Jainish G. R.,Ramesh K.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-2069-1_38

Reference49 articles.

1. IoT: Number of Connected Devices Worldwide 2012–2025 | Statista. Available online: https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/

2. Liu, Y., Yang, C., Jiang, L., Xie, S., Zhang, Y.: Intelligent edge computing for IoT-based energy management in smart cities. IEEE Netw. 33, 111–117 (2019)

3. Ma, X., Wu, Y.-J., Wang, Y., Chen, F., Liu, J.: Mining smart card data for transit riders’ travel patterns. Transp. Res. Part C Emerg. Technol. 36, 1–12 (2013)

4. Derguech, W., Bruke, E., Curry, E.: An autonomic approach to real-time predictive analytics using open data and internet of things. In: UTC-ATCScalCom, pp. 204–211. IEEE (2014)

5. Jakkula, V., Cook, D.: Outlier detection in smart environment structured power datasets. In: Sixth International Conference on Intelligent Environments (IE), pp. 29–33. IEEE (2010)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MR-TBA – An improved Trust-based Security mechanism for IoT Networks;2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS);2024-02-24