Hybrid Energy-Efficient Clustering With Reinforcement Learning for IoT-WSNs Using Knapsack and K -Means

Wireless sensor networks (WSNs) play a fundamental role in the Internet of Things (IoTs), with widespread applications in areas such as smart city infrastructure, industrial control systems, and environmental monitoring. Despite their broad utility, challenges related to energy efficiency and networ...

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Veröffentlicht in:	IEEE sensors journal Jg. 25; H. 15; S. 30047 - 30059
Hauptverfasser:	Aleem, Abdul, Thumma, Rajesh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Cluster analysis Clustering Control systems Data transmission Distributed energy-efficient clustering (DEEC) Energy consumption Energy efficiency energy efficient knapsack algorithm (EEKA) Energy levels Energy management energy optimization Energy states Environmental monitoring Industrial electronics Internet of Things Internet of Things (IoTs) K-means clustering network lifetime Optimization Protocols Real time Resource management Resource utilization Routing Sensors Vector quantization Wireless sensor networks wireless sensor networks (WSNs)
ISSN:	1530-437X, 1558-1748
Online-Zugang:	Volltext
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