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：2019
卷号：15
期号：4
页码：1
DOI：10.1177/1550147719842153
出版社：Hindawi Publishing Corporation
摘要：Wireless sensor networks are designed to perceive, gather, and process external environmental information and send it to the observer. However, the transmission of mass information is a challenge to the sensor nodes. To address this challenge, information fusion technologies are proposed to reduce mass redundant data. However, these techniques rarely consider the historical information, and thereby often encounter the difficulty of low prediction accuracy. In order to solve this difficulty, we propose a novel information fusion approach for the cluster heads. The proposed approach is based on time-recurrent neural network, called sparse long short-term memory, which is derived from the long short-term memory network. The sparse long short-term memory uses sparse matrix to reduce the dimension for a high-dimensional coefficient matrix. Therefore, the computational cost of the fusion algorithm is reduced in wireless sensor networks. The simulation results show that the sparse long short-term memory algorithm increases the survival number of sensor nodes in wireless sensor networks. Furthermore, the prediction accuracy of the sparse long short-term memory algorithm is almost the same as the other comparison algorithms.
关键词：Information fusion; long short-term memory; sparse; wireless sensor networks
