Improving performance of WSNs in IoT applications by transmission power control and adaptive learning rates in reinforcement learning
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11235-024-01191-w.pdf
Reference27 articles.
1. Kumar, D. P., Amgoth, T., & Annavarapu, C. S. R. (2019). Machine learning algorithms for wireless sensor networks: A survey. Information Fusion, 49, 1–25.
2. Osamy, W., Khedr, A. M., Salim, A., AlAli, A. I., & El-Sawy, A. A. (2022). Recent studies utilizing artificial intelligence techniques for solving data collection, aggregation and dissemination challenges in wireless sensor networks: A review. Electronics, 11(3), 313.
3. Sutton, R. S., & Barto, A. G. Reinforcement learning: An introduction, MIT press, 2017.
4. Nayak, P., Swetha, G. K., Gupta, S., & Madhavi, K. (2021). Routing in wireless sensor networks using machine learning techniques: Challenges and opportunities. Measurement, 178, 108974.
5. Frikha, M. S., Gammar, S. M., Lahmadi, A., & Andrey, L. (2021). Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey. Computer Communications, 178, 98–113.
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