Title: Link quality estimation method based on gradient boosting decision tree

Authors: Yan Zhang; Jian Shu

Addresses: School of Software, Nanchang Hangkong University, 696 Fenghe South Avenue, Jiangxi Province, Nanchang, 330063, China ' School of Software, Nanchang Hangkong University, 696 Fenghe South Avenue, Jiangxi Province, Nanchang, 330063, China

Abstract: In the application of wireless sensor networks, link quality estimation is the primary problem to guarantee the reliable transmission of data and the performance of the upper layer network protocol. In order to accurately evaluate link quality, a link quality estimator based on gradient boosting decision tree (GBDT) was proposed. The physical layer parameter average received signal strength indication, mean link quality indicator and mean signal noise rate is selected as the input of the GBDT estimator and the nonlinear correlation between physical layer parameters and packet received rate is analysed by using the maximum information coefficient method. Considering the influence between outliers and different dimensionality of parameters, we used the boxplot method to carry out smoothing and normalisation processing to reduce the complexity of the estimator. At last, the improved particle swarm optimisation algorithm is used to select the optimal parameter combination in the GBDT estimator. The experimental results show that compared with the support vector machine (SVM) estimator, the estimator of this paper has higher accuracy and stability.

Keywords: wireless sensor networks; link quality; GBDT; gradient boosting decision tree; maximum information coefficient; PSO; particle swarm optimisation.

DOI: 10.1504/IJSNET.2021.117232

International Journal of Sensor Networks, 2021 Vol.36 No.3, pp.159 - 166

Accepted: 04 Jan 2021
Published online: 13 Jul 2021 *

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