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

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Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Muteeah Aljawarneh *Computer Science Department, College of Science and Humanities, Dawadmi, Shaqra University, Dawadmi, Riyadh, Saudi Arabia.

Rim Hamdaoui *Computer Science Department, College of Science and Humanities, Dawadmi, Shaqra University, Dawadmi, Riyadh, Saudi Arabia.

Ahmed ZouinkhiMACS Laboratory: Modeling, Analysis and Control of Systems, National Engineering School of Gabes, University of Gabes, Gabes, Tunisia.

Someah AlangariComputer Science Department, College of Science and Humanities, Dawadmi, Shaqra University, Dawadmi, Riyadh, Saudi Arabia.

Mohamed Naceur AbdelkrimMACS Laboratory: Modeling, Analysis and Control of Systems, National Engineering School of Gabes, University of Gabes, Gabes, Tunisia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AccuracyClassificationEnergy optimizationFeature selectionMRMRWSN

Identifiers

PMID38855198

DOI10.7717/peerj-cs.1997

PMCPMC11157571

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