Data Aggregation in WSN based on Deep Self-Encoder

doi:10.23940/ijpe.18.11.p18.27232730

Abstract

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

Lishuang Zhao. Data Aggregation in WSN based on Deep Self-Encoder [J]. Int J Performability Eng, 2018, 14(11): 2723-2730.

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