Enhanced network lifespan in future wireless communication using machine learning based convolution neural networks.
In: Optical & Quantum Electronics, Jg. 56 (2024-04-01), Heft 4, S. 1-12
Online
academicJournal
Zugriff:
A collection of sensor nodes called a wireless sensor network is used to track and document the physical parameters of the surrounding area. The design of network clustering approaches has a big problem when it comes to extending the lifetime of wireless sensor networks (WSNs) and improving energy usage by making sure Both the processing speed and the batteries have a lengthy lifespan. This study presents a framework for machine learning-based channel property variation tracking and learning that is based on a Convolutional Neural Network (CNN)—Long Short-Term Memory (Convolutional-LSTM) network. Our hybrid technique improves sensor connectivity and lowers power consumption, Wireless sensor network longevity is increased. These algorithms are evaluated in a wireless sensor network: Harris Hawks Optimisation (HHO), Coyote Optimisation Algorithm (COY), Support Vector Machine (SVM), and Genetic Algorithm (GA). As for nodes analysis and energy consumption, the article concludes demonstrates the CNN-LSTM technique under consideration outperforms other algorithms. The learning authentication system's robustness and detection performance are thoroughly examined, and exhaustive simulations and testing reveal a notable improvement in the detection accuracy in time-varying scenarios. [ABSTRACT FROM AUTHOR]
Titel: |
Enhanced network lifespan in future wireless communication using machine learning based convolution neural networks.
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Autor/in / Beteiligte Person: | Sheela, S. V. ; Radhika, K. R. |
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Zeitschrift: | Optical & Quantum Electronics, Jg. 56 (2024-04-01), Heft 4, S. 1-12 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 0306-8919 (print) |
DOI: | 10.1007/s11082-023-05943-x |
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