Link Quality Prediction Based On Gradient Boosting

  • Ameena Tijjani Jagamu, Shu Jian, Liu Linlan

Abstract

To predict the consistency of the connections in the Wireless Sensor Network (WSN), which can aid in the
selection of links for the upper layer protocol. In this article we proposed a GBM (Gradient Boosting
Machine)-based prediction of the relation efficiency. This measures the relation parameters (RSSI, LQI, SNR,
and PRR) and trains the model to predict the future value of PRR based on real experimental
data. Experimental findings suggest that our proposed LQP-GBM prediction accuracy indicates the efficacy
of LQP-GBM as compared to CART AND LDA.

Published
2020-04-13
How to Cite
Ameena Tijjani Jagamu, Shu Jian, Liu Linlan. (2020). Link Quality Prediction Based On Gradient Boosting. International Journal of Advanced Science and Technology, 29(7s), 2291-2303. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/12675