Acoustic-Aware BDA-QL: A Hybrid Binary Dragonfly and Q-Learning Framework for Adaptive Clustering in Underwater Wireless Sensor Networks

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:30:03Z
Fecha de publicación2026-01-01
ResumenUnderwater Wireless Sensor Networks (UWSNs) are constrained by limited energy resources, high acoustic propagation delay, and topology variations caused by underwater mobility. This paper proposes a hybrid clustering framework that integrates the Binary Dragonfly Algorithm with Q-learning (BDA-QL) for adaptive cluster-head selection in UWSNs. The proposed method formulates clustering as a binary multi-objective optimization problem considering acoustic-aware energy consumption, end-to-end latency, and cluster load balance. Q-learning is incorporated to dynamically adjust the Dragonfly algorithm coefficients during the optimization stage, while the selected clustering configuration is evaluated under a controlled semicircular mobility model. Simulations were conducted with 100 nodes deployed in a 500 m × 500 m area over 500 simulation rounds and compared against GA, LEACH, C-LEACH, SS-GSO, CDFO-UWSN, and BDA. The results show that BDA-QL preserved the highest number of alive nodes, retaining 59 nodes at the final round, achieved the lowest final latency with 25.43 s, and delivered the highest number of packets, reaching 45,339 packets. BDA-QL provided the strongest overall trade-off across network lifetime, latency, packet delivery, and energy preservation. These findings suggest that reinforcement-learning-based coefficient adaptation can improve the robustness of Dragonfly-based clustering under underwater mobility conditions.es_ES
Doihttps://doi.org/10.3390/technologies14090573es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/1997
Idiomaeses_ES
EditorialMDPI AGes_ES
RelaciónTechnologieses_ES
URL relacionadohttps://doi.org/10.3390/technologies14090573es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteTechnologies
TítuloAcoustic-Aware BDA-QL: A Hybrid Binary Dragonfly and Q-Learning Framework for Adaptive Clustering in Underwater Wireless Sensor Networkses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorVázquez, Eduardo
AutorMéndez, Aldo
AutorGarza, Leopoldo A.
AutorRomero, Gerardo
AutorPanduro, Marco A
AutorElizarraras, Omar
AutorVázquez, Eduardoes_ES
AutorMéndez, Aldoes_ES
AutorGarza, Leopoldo A.es_ES
AutorRomero, Gerardoes_ES
AutorPanduro, Marco Aes_ES
AutorElizarraras, Omares_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número9es_ES
Rango de páginas573es_ES
URL relacionadahttps://doi.org/10.3390/technologies14090573
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen14es_ES

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