Comparing Ensemble Learning Techniques on Data Transmission Reduction for IoT Systems
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-33743-7_6
Reference19 articles.
1. Wang, G., Nixon, M., Boudreaux, M.: Toward cloud-assisted industrial IoT platform for large-scale continuous condition monitoring. Proc. IEEE 107(6), 1193–1205 (2019)
2. Elsayed, W.M., El-Bakry, H.M., El-Sayed, S.M.: Data reduction using integrated adaptive filters for energy-efficient in the clusters of wireless sensor networks. IEEE Embed. Syst. Lett. 11(4), 119–122 (2019)
3. Yemeni, Z., Wang, H., Ismael, W.M., Wang, Y., Chen, Z.: Reliable spatial and temporal data redundancy reduction approach for WSN. Comput. Netw. 185, 107701 (2021)
4. Fathalla, A., Li, K., Salah, A., Mohamed, M.F.: An LSTM-based distributed scheme for data transmission reduction of IoT systems. Neurocomputing 485, 166–180 (2022)
5. Jarwan, A., Sabbah, A., Ibnkahla, M.: Data transmission reduction schemes in WSNs for efficient IoT systems. IEEE J. Sel. Areas Commun. 37(6), 1307–1324 (2019). https://doi.org/10.1109/JSAC.2019.2904357
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