期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2015
卷号：2015
DOI：10.1155/2015/828493
出版社：Hindawi Publishing Corporation
摘要：A core factor to consider when designing wireless sensor networks is the reliable and efficient transmission of massive data from source to destination. In practical situations, data transmission is often disrupted by link interference and interruption resulting in the data losses. Link quality prediction is an important approach to solve this problem. By estimating the link quality based on the past knowledge and information, link quality prediction is essential for routing decisions of future data transmission. Traditional link quality prediction algorithms are simply based on the statistical information of the links in the wireless sensor network. By introducing complex network theory and machine learning techniques, we propose a neighborhood-based nonnegative matrix factorization model to predict link quality in wireless sensor networks. Our model learns latent features of the nodes from the information of past data transmissions combing with local neighborhood structures of the underlying network topology and then estimates the link quality depending on the common latent features of the two nodes between the link. Extensive experiments on both real-world networks and simulation networks demonstrate the effectiveness and efficiency of our proposed model.
