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

Jianlin Liu1, Fenxiong Chen2, Dianhong Wang3

  • 1School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China. liujianlin@cug.edu.cn.

Sensors (Basel, Switzerland)
|December 7, 2018
PubMed
Summary
This summary is machine-generated.

A new Stacked RBM Auto-Encoder model significantly compresses data in wireless sensor networks (WSNs), reducing energy consumption by 90%. This method enhances sensor node lifetime through efficient data compression and reconstruction.

Keywords:
data compressionenergy consumption optimizationstacked RBMtransfer learningwireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) face significant energy constraints due to data communication.
  • Extending the operational lifetime of sensor nodes is critical and often achieved by minimizing data transmission.

Purpose of the Study:

  • To introduce a novel Stacked RBM Auto-Encoder (Stacked RBM-AE) model for effective data compression in WSNs.
  • To enhance energy efficiency through model optimization and parameter pruning.

Main Methods:

  • Developed a Stacked RBM-AE model with encode and decode layers, each comprising four Restricted Boltzmann Machines (RBMs).
  • Implemented an energy optimization technique involving parameter pruning to reduce computational and storage overhead.
  • Evaluated the model using environmental data from Intel Lab.

Main Results:

  • Achieved a compression ratio of 10 with an average Percentage RMS Difference of 10.04% and temperature reconstruction error of 0.2815 °C.
  • Demonstrated a 90% reduction in node communication energy consumption in WSNs.
  • Outperformed traditional methods in compression efficiency and reconstruction accuracy.

Conclusions:

  • The proposed Stacked RBM-AE model offers superior data compression and reconstruction for WSNs.
  • The model exhibits high compression efficiency and valuable transfer learning capabilities.
  • This approach effectively prolongs sensor node lifetime by minimizing energy expenditure.