Scholarlink

Effective energy management is essential for wireless sensor networks to maintain seamless operation and increase network lifetime. Proper placement of chargers is an important mechanism for attaining this. In this research, the Dragonfly Algorithm, a nature-inspired optimization approach founded on swarming behaviors of dragonflies, is utilized for charger deployment optimization. Dragonfly Algorithm simulates social interactions for navigation, search for food, and evading enemies to better explore and exploit. The simulation outcomes emphasize the better performance of the Dragonfly Algorithm in charger placement optimization, minimization of redundancy, and optimization of network sustainability. Dragonfly Algorithm has a performance rate of 95.01%, ranking higher than well-established methods such as Haar (91.11%), Daubechies 2 (91.26%), Biorthogonal (92.01%), and Symlets 8 (92.16%) wavelets in dealing with sensor energy fluctuations and charger placement issues. This research sets the Dragonfly Algorithm as a potential energy-efficient wireless sensor network management.

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Efficient Charger Deployment in Wireless Sensor Networks Using Dragonfly Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • P. Neelagandan,
  • S. Balaji,
  • R. Pavithra

摘要

Effective energy management is essential for wireless sensor networks to maintain seamless operation and increase network lifetime. Proper placement of chargers is an important mechanism for attaining this. In this research, the Dragonfly Algorithm, a nature-inspired optimization approach founded on swarming behaviors of dragonflies, is utilized for charger deployment optimization. Dragonfly Algorithm simulates social interactions for navigation, search for food, and evading enemies to better explore and exploit. The simulation outcomes emphasize the better performance of the Dragonfly Algorithm in charger placement optimization, minimization of redundancy, and optimization of network sustainability. Dragonfly Algorithm has a performance rate of 95.01%, ranking higher than well-established methods such as Haar (91.11%), Daubechies 2 (91.26%), Biorthogonal (92.01%), and Symlets 8 (92.16%) wavelets in dealing with sensor energy fluctuations and charger placement issues. This research sets the Dragonfly Algorithm as a potential energy-efficient wireless sensor network management.