Lightweight Multi-parameter Fusion Link Quality Estimation Based on Weighted Euclidean Distance

Abstract

Real-time and accurate link quality estimation is critical for efficient routing in wireless sensor networks. Mapping relations between physical layer parameters and packet reception ratio can be used for link quality estimation, which has the advantages of high agility and low overhead. However, existing low complexity estimators take only one physical layer parameter into consideration, which makes them difficult to describe real link quality accurately. In this paper, a lightweight, weighted Euclidean distance based multi-parameter fusion link quality estimator is proposed. Two physical layer parameters, Signal-to-Noise Ratio and Link Quality Indicator are combined with weighted Euclidean distance. Then, link quality is estimated quantitatively with mapping relation between the fused parameter and packet reception ratio, which is constructed by logistic regression. This lightweight multi-parameter fusion method combines advantages of Signal-to-Noise Ratio and Link Quality Indicator under different link qualities, so it could achieve more accurate estimations. Compared with similar estimators, the estimate error of the proposed one is reduced by 13.26%~39.75% under different link qualities and by at least 18.20% in long time links.