Sparse long short-term memory for information fusion in wireless sensor networks

Yangfan Zhou,Ping Xie,Qingtao Wu,Ruijuan Zheng,Junlong Zhu,Mingchuan Zhang

doi:10.1177/1550147719842153

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 algorith...

Highlights

Wireless sensor networks (WSNs) adapt to many demanding environments due to the development of sensor technology and microelectronics industry, for example, military affairs, environmental monitoring, forest fire prevention, and traffic control.[1,2] the transmission of mass observed data shortens the life of sensor nodes, which is a challenge of WSNs.[3]
We propose a sparse model based on long short-term memory (LSTM) algorithm, that is, sparse long short-term memory (SLSTM)
The results show that our SLSTM is excellent, which saves a lot of energy for WSN and thereby lengthens the life of WSN

Summary

Introduction

Wireless sensor networks (WSNs) adapt to many demanding environments due to the development of sensor technology and microelectronics industry, for example, military affairs, environmental monitoring, forest fire prevention, and traffic control.[1,2] the transmission of mass observed data shortens the life of sensor nodes, which is a challenge of WSNs.[3]. Chen et al.[5] proposed a fusion method combining channel perception and likelihood ratio, which does not need the perfect knowledge. In order to avoid random packet loss in sensor networks, John and Vorontsov[6] and Tsanas et al.[7] considered information fusion in terms of an estimation method. Some fusion technologies based on the clustering algorithm were proposed in previous works.[10,11,12] some online algorithms were proposed in sensor networks.[13]. There is a lot of useful information in the previous observations For this reason, this article proposes a novel neural network approach based on the historical information. The approach utilizes long short-term memory (LSTM) networks to retain the useful parts of historical data and discard the useless parts.

Related work

Results and discussion

Conclusion