Link quality estimation based on over-sampling and weighted random forest

Linlan Liu1, Yi Feng3, 1, Shengrong Gao1 and Jian Shu2

  1. School of Information Engineering, Nanchang Hangkong University
    330063 Nanchang, China
    765693987@qq.com, 1322415547@qq.com
  2. School of Software, Nanchang Hangkong University
    330063 Nanchang, China
    shujian@nchu.edu.cn
  3. School of Engineering, Zhejiang Normal University Xingzhi College
    321000 Jinhua, China
    458018002@qq.com

Abstract

Aiming at the imbalance problem of wireless link samples, we propose the link quality estimation method which combines the K-means synthetic minority over-sampling technique (K-means SMOTE) and weighted random forest. The method adopts the mean, variance and asymmetry metrics of the physical layer parameters as the link quality parameters. The link quality is measured by link quality level which is determined by the packet receiving rate. K-means is used to cluster link quality samples. SMOTE is employed to synthesize samples for minority link quality samples, so as to make link quality samples of different link quality levels reach balance. Based on the weighted random forest, the link quality estimation model is constructed. In the link quality estimation model, the decision trees with worse classification performance are assigned smaller weight, and the decision trees with better classification performance are assigned bigger weight. The experimental results show that the proposed link quality estimation method has better performance with samples processed by K-means SMOTE. Furthermore, it has better estimation performance than the ones of Naive Bayesian, Logistic Regression and K-nearest Neighbour estimation methods.

Key words

Wireless Sensor Network, Link Quality Estimation, Weighted Random Forest, Oversampling

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS201218041L

How to cite

Liu, L., Feng, Y., Gao, S., Shu, J.: Link quality estimation based on over-sampling and weighted random forest. Computer Science and Information Systems, https://doi.org/10.2298/CSIS201218041L