Leveraging MAC Preambles for an Efficient Link Estimation

Rojas, Camilo; Decotignie, Jean-Dominique

doi:10.1109/wimob.2018.8589096

Cited by 1 publication

(1 citation statement)

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“…Estimating the quality of a link is a key primitive in WSNs, as upper layers use this piece of information in making performance-critical decisions. Literature [15] proposed Rep, a novel sampling scheme able to extract the link quality from the packet repetitions of low-power preamble sampling MACs. The experiments show that Rep reduces the energy and traffic used for link estimation by one order of magnitude, and increases the speed of the process by one order of magnitude, while maintaining state-of-the-art accuracy.…”

Section: 2mentioning

confidence: 99%

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

2022

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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.

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Section: 2mentioning

confidence: 99%