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Data Aggregation in WSN based on Deep Self-Encoder

Volume 14, Number 11, November 2018, pp. 2723-2730
DOI: 10.23940/ijpe.18.11.p18.27232730

Lishuang Zhaoa,b

aCollege of Information Science and Technology, Bohai University, Jinzhou, 121000, China
bCollege of Information Science and Engineering, Northeastern University, Shenyang, 110004, China

(Submitted on August 7, 2018; Revised on September 5, 2018; Accepted on October 19, 2018)


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.


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