A Machine Learning Perspective of Optimal Data Transmission in Wireless Sensor Networks (WSN)
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47942-7_15
Reference21 articles.
1. Ding, Q., Zhu, R., Liu, H., Ma, M.: An overview of machine learning-based energy-efficient routing algorithms in wireless sensor networks. Electronics 10(13), 1539 (2021)
2. Gillani, M., Niaz, H.A., Tayyab, M.: Role of machine learning in WSN and VANETs. Int. J. Electr. Comput. Eng. Res. 1(1), 15–20 (2021)
3. Guo, W., Yan, C., Lu, T.: Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing. Int. J. Distrib. Sensor Netw. 15(2), 1550147719833541 (2019)
4. Hajjej, F., Hamdi, M., Ejbali, R., Zaied, M.: A distributed coverage hole recovery approach based on reinforcement learning for Wireless Sensor Networks. Ad Hoc Netw. 101, 102082 (2020)
5. http://www.topology-zoo.org/dataset.html
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