Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse Autoencoder

Babajide O. Ayinde*, Abdulaziz Y. Barnawi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


The energy in wireless sensor networks is considered a scarce commodity, especially in scenarios where it is difficult or impossible to provide supplementary energy sources once the initially available energy is used up. Even in cases where energy harvesting is feasible, effective energy utilization is still a crucial step for prolonging the network lifetime. Enhancement of life-time through efficient energy management is one of the essential ingredients underlining the design of any credible wireless sensor network. In this paper, we propose a sensor selection method using a novel and unsupervised neural network structure referred to as partly-informed sparse autoencoder (PISAE) that aims to reconstruct all sensor readings from a select few. The PISAE comprises three submodules, namely: the gate (which selects the most important sensors), encoder (encodes and compresses the data from select sensors), and decoder (decodes the output of the encoder and regenerates the readings of all initial sensors). Our approach relies on the premise that many sensors are redundant because their readings are spatially and temporally correlated and are predictable from the readings of a few other sensors in the network. Thus, overall network reliability and lifetime are enhanced by putting sensors with redundant readings to sleep without losing significant information. We evaluate the efficacy of the proposed method on three benchmark datasets and compare with existing results. The experimental results indicate the superiority of our approach compared with existing approaches in terms of accuracy and lifetime extension factor.

Original languageEnglish
Article number8716655
Pages (from-to)63346-63360
Number of pages15
JournalIEEE Access
StatePublished - 2019

Bibliographical note

Funding Information:
This work was supported by the King Fahd University of Petroleum and Minerals.

Publisher Copyright:
© 2013 IEEE.


  • Autoencoder
  • deep learning
  • energy conservation
  • energy management
  • feature importance
  • feature ranking
  • lifetime extension
  • unsupervised feature extraction
  • wireless sensor network

ASJC Scopus subject areas

  • Engineering (all)
  • Materials Science (all)
  • Computer Science (all)


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