Integration of Prediction Based Hybrid Compression in Distributed Sensor Network
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-021-08896-0.pdf
Reference19 articles.
1. David, L. (2009). Donoho, “Compressed sensing.” IEEE Transactions On Information Theory, 52, 1289–1306.
2. Machiwal D., & Jha, M. K. (2012) Stochastic modelling of time series. In: Hydrologic time series analysis: Theory and Practice. Dordrecht: Springer. https://doi.org/10.1007/978-94-007-1861-6_5
3. Tealab, A. (2018). Time series forecasting using artificial neural networks methodologies: A systematic review. Future Computing and Informatics Journal, 3(2), 334–340.
4. Yadav, S., & Kumar, V. (2019). Hybrid compressive sensing enabled energy efficient transmission of multi-hop clustered UWSNs. AEU-International Journal of Electronics and Communications, 110, 152836.
5. Srisooksai, T., Keamarungsi, K., Lamsrichan, P., & Araki, K. (2012). Practical data compression in wireless sensor networks: A survey. Journal of Network and Computer Applications, ELSEVIER, 35, 37–59.
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