PeerJ Computer Science (Apr 2024)

Energy optimization for wireless sensor network using minimum redundancy maximum relevance feature selection and classification techniques

  • Muteeah Aljawarneh,
  • Rim Hamdaoui,
  • Ahmed Zouinkhi,
  • Someah Alangari,
  • Mohamed Naceur Abdelkrim

DOI
https://doi.org/10.7717/peerj-cs.1997
Journal volume & issue
Vol. 10
p. e1997

Abstract

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In wireless sensor networks (WSN), conserving energy is usually a basic issue, and several approaches are applied to optimize energy consumption. In this article, we adopt feature selection approaches by using minimum redundancy maximum relevance (MRMR) as a feature selection technique to minimize the number of sensors thereby conserving energy. MRMR ranks the sensors according to their significance. The selected features are then classified by different types of classifiers; SVM with linear kernel classifier, naïve Bayes classifier, and k-nearest neighbors classifier (KNN) to compare accuracy values. The simulation results illustrated an improvement in the lifetime extension factor of sensors and showed that the KNN classifier gives better results than the naïve Bayes and SVM classifier.

Keywords