A Link Quality Estimation Method Based on Improved Weighted Extreme Learning Machine

The link quality of wireless sensor networks is the basis for selecting communication links in routing protocols. Effective link quality estimation is helpful to select high-quality links for communication and to improve network stability. The correlation of link quality parameter and packet reception rate (PRR) is calculated by the Pearson correlation coefficient. According to Pearson coefficient values, the averages of the link quality indication, received signal strength indication, and signal-to-noise are selected as the parameters of the link quality. The link quality grade is taken as a metric of the link quality estimation. Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters of the weighted extreme learning machine (WELM), including the number of hidden nodes, weights, and the normalization factor. A link quality estimator (LQE) based on the improved weighted extreme learning machine (LQE-IWELM) is constructed. In different scenarios, experiment results show that the improved weighted extreme learning machine (IWELM) is more effective than extreme learning machine (ELM) and WELM. Compared with the other three link quality estimation models, LQE-IWELM has better precision and G_mean.