Int J Performability Eng ›› 2018, Vol. 14 ›› Issue (11): 2723-2730.doi: 10.23940/ijpe.18.11.p18.27232730

Previous Articles     Next Articles

Data Aggregation in WSN based on Deep Self-Encoder

Lishuang Zhaoa, b, *   

  1. a College of Information Science and Technology, Bohai University, Jinzhou, 121000, China;
    b College of Information Science and Engineering, Northeastern University, Shenyang, 110004, China
  • Submitted on ;
  • Contact: * E-mail address:
  • About author:Lishuang Zhao received the M.S. degree in computer education area from Bohai University in 2008, She is an associate professor at Bohai University. She is studying for a doctor's degree in NEU. Her research interests include Embedded System, Intelligent Data Processing, etc.

Abstract: In order to reduce the energy consumption of data transmission in the limited resources of wireless sensor networks, a WSN data fusion algorithm based on AEDA) is proposed. Firstly, the deep self-encoder (DESAE) is constructed and the training is completed at the sink node, and the trained parameters are passed to the corresponding sensor nodes. The algorithm proposes two kinds of data fusion models, which can extract the raw data through the network model to obtain a small amount of feature data and send it to the sink node, reducing the amount of data transmission. The simulation results show that compared with the LEACH algorithm, this algorithm can significantly reduce the energy consumption, extend the network life cycle, and is more suitable for large-scale networks.

Key words: wireless sensor networks, data fusion, deep self-encoder, feature data, life cycle