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<p>Dynamic underwater wireless sensor networks (UWSNs) play a crucial role in marine monitoring and surveillance, but their performance is severely constrained by limited energy resources and harsh environmental dynamics. This paper studies the Maximum Lifetime Target Coverage (MLTC-UWSN) problem, which aims to prolong network lifetime while guaranteeing complete target coverage and connectivity under a probabilistic sensing model. We propose EGA-MLTC, an enhanced genetic algorithm that integrates improved crossover, mutation, and diversity-preserving mechanisms within a key-time scheduling framework, enabling effective extraction of disjoint sensor covers that adapt to sensor state variations. The proposed approach is evaluated against baseline metaheuristics, including Differential Evolution (DE) and Harmony Search (HMS), under diverse network scales, sensor densities, and environmental conditions. Simulation results show that EGA-MLTC consistently achieves longer operational lifetimes and more robust coverage than competing methods. These findings establish EGA-MLTC as a practical and scalable solution for efficient and reliable operation of dynamic UWSNs.</p>

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Maximizing Lifetime and Target Coverage in Dynamic Underwater Wireless Sensor Networks Using an Enhanced Genetic Algorithm

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • Nguyen Thi My Binh,
  • Tran Son Tung,
  • Tran Le Dung,
  • Ho Viet Duc Luong

摘要

Dynamic underwater wireless sensor networks (UWSNs) play a crucial role in marine monitoring and surveillance, but their performance is severely constrained by limited energy resources and harsh environmental dynamics. This paper studies the Maximum Lifetime Target Coverage (MLTC-UWSN) problem, which aims to prolong network lifetime while guaranteeing complete target coverage and connectivity under a probabilistic sensing model. We propose EGA-MLTC, an enhanced genetic algorithm that integrates improved crossover, mutation, and diversity-preserving mechanisms within a key-time scheduling framework, enabling effective extraction of disjoint sensor covers that adapt to sensor state variations. The proposed approach is evaluated against baseline metaheuristics, including Differential Evolution (DE) and Harmony Search (HMS), under diverse network scales, sensor densities, and environmental conditions. Simulation results show that EGA-MLTC consistently achieves longer operational lifetimes and more robust coverage than competing methods. These findings establish EGA-MLTC as a practical and scalable solution for efficient and reliable operation of dynamic UWSNs.