Author:
Mahalakshmi G.,Ramalingam S.,Manikandan A.
Publisher
Springer Science and Business Media LLC
Reference34 articles.
1. Patil, G., & Kaneez Rabiya Quadri, S. (2022). Deep learning fault detection algorithms in WSNs. International Research Journal of Engineering and Technology (IRJET), 09 (09). e-ISSN: 2395-0056. www.irjet.net. p-ISSN: 2395-0072.
2. Biswas, P., & Samanta, T. (2021). A method for fault detection in wireless sensor network based on Pearson's correlation coefficient and support vector machine classification, preprint (Version 1) available at Research Square https://doi.org/10.21203/rs.3.rs-380070/v1.
3. Mahmood, T., Li, J., Pei, Y., Akhtar, F., Butt, S. A., Ditta, A., & Qureshi, S. (2021). An intelligent fault detection approach based on reinforcement learning system in wireless sensor network. The Journal of Supercomputing. https://doi.org/10.1007/s11227-021-04001-1
4. Amutha, J., Sharma, S., & Sharma, S. K. (2021). Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: Review, taxonomy, research fndings, challenges and future directions. Computer Science Review, 40, 1–45.
5. Kanoun, O., Bradai, S., Khriji, S., Bouattour, G., El Houssaini, D., Ben Ammar, M., Naifar, S., Bouhamed, A., Derbel, F., & Viehweger, C. (2021). Energy-aware system design for autonomous wireless sensor nodes: A comprehensive review. Sensors, 21(2), 1–25.
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