Study on AdaBoost-based link quality prediction mechanism - Jian SHU; Man-lan LIU; Wei ZHENG (Tongxin xuebao)
The link quality was vulnerable to the complexity environment in wireless sensor network.Obtaining link quality information in advance could reduce energy consumption of nodes.After analyzing the existing link quality prediction methods,AdaBoost-based link quality prediction mechanism was put forward.Link quality samples in deferent scenarios were collected.Density-based unsupervised clustering algorithm was employed to classify training samples into deferent link quality levels.The AdaBoost with SVM-based component classifiers was adopted to build link quality prediction mechanism.Experimental results show that the proposed mechanism has better prediction precision.