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<p>The sustainability of wireless sensor networks critically depends on intelligent and efficient charger deployment. This paper proposes a two-stage optimization framework that determine the total number of chargers needed to recharge and the optimal position of charger. In the first stage, a hybrid algorithm combining degree-based saturation and Grundy coloring efficiently determines the minimal number of chargers required. In the second stage, an Enhanced Aquila Optimization algorithm, inspired by eagle hunting behavior, identifies optimal charger locations under coverage and power constraints. Experimental results show that the enhancement method improves coverage by 6% compared to the standard algorithm. The Enhanced Aquila Optimization achieves 99% sensor coverage with faster convergence than Raindrop (82%), Grey Wolf (85%), Black Hole (89%), Dragonfly (89%), and standard Aquila Optimization (93%). Statistical analysis confirms the significant performance difference between the proposed and existing algorithms. Overall, the proposed approach provides a practical, scalable, and energy-efficient solution for sustainable wireless sensor network deployment.</p>

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Charger placement optimization in wireless sensor networks using hybrid graph coloring and Enhanced Aquila Optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月26日

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  • P. Neelagandan,
  • S. Balaji

摘要

The sustainability of wireless sensor networks critically depends on intelligent and efficient charger deployment. This paper proposes a two-stage optimization framework that determine the total number of chargers needed to recharge and the optimal position of charger. In the first stage, a hybrid algorithm combining degree-based saturation and Grundy coloring efficiently determines the minimal number of chargers required. In the second stage, an Enhanced Aquila Optimization algorithm, inspired by eagle hunting behavior, identifies optimal charger locations under coverage and power constraints. Experimental results show that the enhancement method improves coverage by 6% compared to the standard algorithm. The Enhanced Aquila Optimization achieves 99% sensor coverage with faster convergence than Raindrop (82%), Grey Wolf (85%), Black Hole (89%), Dragonfly (89%), and standard Aquila Optimization (93%). Statistical analysis confirms the significant performance difference between the proposed and existing algorithms. Overall, the proposed approach provides a practical, scalable, and energy-efficient solution for sustainable wireless sensor network deployment.