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<p>Efficient energy replenishment in wireless sensor networks is essential for sustaining prolonged network operation and ensuring the reliability of sensor-based monitoring applications. A major challenge lies in determining both the minimum number of chargers required and their most effective deployment locations to guarantee full sensor coverage while minimizing operational costs. This study presents a two-stage optimization approach to address this problem. First, an Iterative Max-Degree Grouping method is employed to determine the minimum number of chargers required to recharge all sensors, based on connectivity. Second, the optimal placement of these chargers is determined using the Chimp Optimization Algorithm, a recent metaheuristic inspired by the cooperative hunting behavior of chimpanzees. Chimp Optimization Algorithm models four behavioral roles−driver, chaser, attacker, and barrier−to efficiently balance exploration and exploitation during the search for optimal solutions. A custom fitness function is formulated to evaluate charger positions based on sensor coverage and energy efficiency. Simulation results reveal that Chimp Optimization Algorithm (97%) significantly outperforms traditional and state-of-the-art algorithms−including Random (72%), Raindrop (88%), Quokka (90%), Grey wolf algorithm (92%), and Artificial Bee Colony (95%)−in terms of coverage percentage and convergence rate. These results highlight the effectiveness of the proposed framework in enhancing energy sustainability in wireless sensor networks.</p>

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A Hybrid Efficient Wireless Sensor Recharging Framework Using Iterative Max-Degree Grouping and the Chimp Optimization Algorithm

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Neelagandan P,
  • Balaji S

摘要

Efficient energy replenishment in wireless sensor networks is essential for sustaining prolonged network operation and ensuring the reliability of sensor-based monitoring applications. A major challenge lies in determining both the minimum number of chargers required and their most effective deployment locations to guarantee full sensor coverage while minimizing operational costs. This study presents a two-stage optimization approach to address this problem. First, an Iterative Max-Degree Grouping method is employed to determine the minimum number of chargers required to recharge all sensors, based on connectivity. Second, the optimal placement of these chargers is determined using the Chimp Optimization Algorithm, a recent metaheuristic inspired by the cooperative hunting behavior of chimpanzees. Chimp Optimization Algorithm models four behavioral roles−driver, chaser, attacker, and barrier−to efficiently balance exploration and exploitation during the search for optimal solutions. A custom fitness function is formulated to evaluate charger positions based on sensor coverage and energy efficiency. Simulation results reveal that Chimp Optimization Algorithm (97%) significantly outperforms traditional and state-of-the-art algorithms−including Random (72%), Raindrop (88%), Quokka (90%), Grey wolf algorithm (92%), and Artificial Bee Colony (95%)−in terms of coverage percentage and convergence rate. These results highlight the effectiveness of the proposed framework in enhancing energy sustainability in wireless sensor networks.