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Sensors 2018, 18(12), 4273; https://doi.org/10.3390/s18124273

Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks

School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
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Received: 6 November 2018 / Revised: 27 November 2018 / Accepted: 1 December 2018 / Published: 4 December 2018
(This article belongs to the Section Sensor Networks)
PDF [1872 KB, uploaded 4 December 2018]   |   Review Reports

Abstract

Data compression is very important in wireless sensor networks (WSNs) with the limited energy of sensor nodes. Data communication results in energy consumption most of the time; the lifetime of sensor nodes is usually prolonged by reducing data transmission and reception. In this paper, we propose a new Stacked RBM Auto-Encoder (Stacked RBM-AE) model to compress sensing data, which is composed of a encode layer and a decode layer. In the encode layer, the sensing data is compressed; and in the decode layer, the sensing data is reconstructed. The encode layer and the decode layer are composed of four standard Restricted Boltzmann Machines (RBMs). We also provide an energy optimization method that can further reduce the energy consumption of the model storage and calculation by pruning the parameters of the model. We test the performance of the model by using the environment data collected by Intel Lab. When the compression ratio of the model is 10, the average Percentage RMS Difference value is 10.04%, and the average temperature reconstruction error value is 0.2815 °C. The node communication energy consumption in WSNs can be reduced by 90%. Compared with the traditional method, the proposed model has better compression efficiency and reconstruction accuracy under the same compression ratio. Our experiment results show that the new neural network model can not only apply to data compression for WSNs, but also have high compression efficiency and good transfer learning ability.
Keywords: wireless sensor networks; data compression; stacked RBM; transfer learning; energy consumption optimization wireless sensor networks; data compression; stacked RBM; transfer learning; energy consumption optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Liu, J.; Chen, F.; Wang, D. Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks. Sensors 2018, 18, 4273.

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