Energy-aware Clustering and Lifetime Extension in WSNs Using Evolutionary Learning Algorithms
Abstract Nodes in Wireless Sensor Networks (WSNs) are characterized by limited resources, including energy, memory, and processing power, which necessitates optimized energy usage to extend the network’s lifespan. Efficient CH selection significantly contributes to conserving energy and improving th...
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| Autori principali: | , , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Springer
2026-04-01
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| Serie: | International Journal of Computational Intelligence Systems |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/s44196-026-01284-1 |
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