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