Paper Info
Title
Sparse Long Short-Term Memory For Information Fusion In Wireless Sensor Networks
Abstract
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.
Year
DOI
Venue
2019
10.1177/1550147719842153
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
Keywords
Field
DocType
Information fusion, long short-term memory, sparse, wireless sensor networks
Coefficient matrix,Computer science,Fusion,Long short term memory,Real-time computing,Observer (quantum physics),Artificial neural network,Wireless sensor network,Information fusion,Sparse matrix,Distributed computing
Journal
Volume
Issue
ISSN
15
4
1550-1477
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Yangfan Zhou123229.72
Mingchuan Zhang2124.64
Ping Xie34017.27
Junlong Zhu43714.28
Ruijuan Zheng55114.47
Qingtao Wu67019.88