Energy optimization for wireless sensor network using minimum redundancy maximum relevance feature selection and classification techniques Article Swipe
Muteeah Aljawarneh
,
Rim Hamdaoui
,
Ahmed Zouinkhi
,
Someah Alangari
,
Mohamed Naceur Abdelkrim
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.1997
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.1997
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.
Related Topics
Concepts
Computer science
Feature selection
Support vector machine
Naive Bayes classifier
Classifier (UML)
Artificial intelligence
Pattern recognition (psychology)
Wireless sensor network
Redundancy (engineering)
Quadratic classifier
Bayes classifier
Bayes' theorem
Margin classifier
Data mining
Machine learning
Bayesian probability
Computer network
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7717/peerj-cs.1997
- OA Status
- gold
- Cited By
- 7
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396519114
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396519114Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7717/peerj-cs.1997Digital Object Identifier
- Title
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Energy optimization for wireless sensor network using minimum redundancy maximum relevance feature selection and classification techniquesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-30Full publication date if available
- Authors
-
Muteeah Aljawarneh, Rim Hamdaoui, Ahmed Zouinkhi, Someah Alangari, Mohamed Naceur AbdelkrimList of authors in order
- Landing page
-
https://doi.org/10.7717/peerj-cs.1997Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.7717/peerj-cs.1997Direct OA link when available
- Concepts
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Computer science, Feature selection, Support vector machine, Naive Bayes classifier, Classifier (UML), Artificial intelligence, Pattern recognition (psychology), Wireless sensor network, Redundancy (engineering), Quadratic classifier, Bayes classifier, Bayes' theorem, Margin classifier, Data mining, Machine learning, Bayesian probability, Computer network, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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