journal article·2022Open access

Hybrid Sensor Selection Technique for Lifetime Extension of Wireless Sensor Networks

Khaled M. Fouad, Basma M. Hassan, Omar M. Salim

Computers, Materials & Continua· Volume 70· Issue 3

Abstract

Energy conservation is a crucial issue to extend the lifetime of wireless sensor networks (WSNs) where the battery capacity and energy sources are very restricted. Intelligent energy-saving techniques can help designers overcome this issue by reducing the number of selected sensors that report environmental measurements by eliminating all replicated and unrelated features. This paper suggests a Hybrid Sensor Selection (HSS) technique that combines filter-wrapper method to acquire a rich-informational subset of sensors in a reasonable time. HSS aims to increase the lifetime of WSNs by using the optimal number of sensors. At the same time, HSS maintains the desired level of accuracy and manages sensor failures with the most suitable number of sensors without compromising the accuracy. The evaluation of the HSS technique has adopted four experiments by using four different datasets. These experiments show that HSS can extend the WSNs lifetime and increase the accuracy using a sufficient number of sensors without affecting the WSN functionality. Furthermore, to ensure HSS credibility and reliability, the proposed HSS technique has been compared to other corresponding methodologies and shows its superiority in energy conservation at premium accuracy measures.
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Research context

OpenAlex topics: Energy Efficient Wireless Sensor Networks · Distributed Sensor Networks and Detection Algorithms · Water Quality Monitoring Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (22)

Show source-provided references
1. Machine learning in wireless sensor networks: Algorithms, strategies, and applications10.1109/COMST.2014.2320099 ↗
2. Energy efficiency in internet of things: An overview
3. Machine learning in wireless sensor networks: Challenges and opportunities
4. An optimized k-nearest neighbor algorithm for extending wireless sensor network lifetime10.1007/978-3-319-74690-6_50 ↗
5. Fault detection in wireless sensor networks through SVM classifier10.1109/JSEN.2017.2771226 ↗
6. Fault detection in wireless sensor networks through the random forest classifier10.3390/s19071568 ↗
7. Intelligent approach for large-scale data mining10.1504/IJCAT.2020.107906 ↗
8. Energy management in wireless sensor networks based on naive Bayes, MLP, and SVM classifications: A comparative study10.1155/2016/6250319 ↗
9. Laplacian score for feature selection
10. Hybrid detection algorithm for online faulty sensors identification in wireless sensor networks10.1049/iet-wss.2020.0053 ↗
11. Intelligent system for feature selection based on rough set and chaotic binary grey wolf optimisation10.1504/IJCAT.2020.107901 ↗
12. Energy conservation in wireless sensor networks using partly-informed sparse autoencoder10.1109/ACCESS.2019.2917322 ↗
13. Feature selection and energy management for wireless sensor networks
14. Clustering-based distributed support vector machine in wireless sensor networks
15. Energy management of wireless sensor networks based on multi-layer perceptrons
16. Machine learning-based energy-saving framework for environmental states-adaptive wireless sensor network10.1109/ACCESS.2020.2986507 ↗
17. Fisher score and matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines10.1007/s10115-018-1185-y ↗
18. Analysis of variance: The fundamental concepts10.1179/jmt.2009.17.2.27E ↗
19. Wrappers for feature subset selection10.1016/S0004-3702(97)00043-X ↗
20. Genetic algorithm and confusion matrix for document clustering
21. A. Frank and A. Asuncion, “UCI machine learning repository,” Irvine, CA, University of California, School of Information and Computer Science, Accessed on Feb. 09, 2021. [Online]. Available: https://archive.ics.uci.edu/ml/index.php.
22. A systematic analysis of performance measures for classification tasks10.1016/j.ipm.2009.03.002 ↗