Scholarlink

<p>Optimizing energy efficiency and ensuring reliable communication are critical challenges in modern sensor networks deployed in resource-constrained environments. This paper introduces a novel bio-inspired optimization algorithm that leverages dynamic exploration and exploitation phases to enhance network performance. Inspired by natural behaviors, the exploration phase employs detour foraging to identify diverse routing paths, while the exploitation phase utilizes random hiding strategies to refine the optimal solution. The algorithm’s convergence is facilitated by an adaptive energy factor that gradually transitions the focus from exploration to exploitation, ensuring both global search diversity and local refinement. The proposed ARO method improves network lifetime by 113%, throughput by 160%, and residual energy retention by 30% compared to baseline protocols. Simulation results validate the framework’s effectiveness, demonstrating significant improvements in energy utilization, throughput, and overall network longevity compared to state-of-the-art methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial Rabbit Optimization Algorithm for Enhancing Energy Efficiency and Routing Reliability in Wireless Sensor Networks

  • 期刊论文

  • 发表日期：

    2025年10月22日

查看全文
  • Yash Veer Singh,
  • Prasant Singh Yadav,
  • Shadab Khan,
  • Prashant Tomer

摘要

Optimizing energy efficiency and ensuring reliable communication are critical challenges in modern sensor networks deployed in resource-constrained environments. This paper introduces a novel bio-inspired optimization algorithm that leverages dynamic exploration and exploitation phases to enhance network performance. Inspired by natural behaviors, the exploration phase employs detour foraging to identify diverse routing paths, while the exploitation phase utilizes random hiding strategies to refine the optimal solution. The algorithm’s convergence is facilitated by an adaptive energy factor that gradually transitions the focus from exploration to exploitation, ensuring both global search diversity and local refinement. The proposed ARO method improves network lifetime by 113%, throughput by 160%, and residual energy retention by 30% compared to baseline protocols. Simulation results validate the framework’s effectiveness, demonstrating significant improvements in energy utilization, throughput, and overall network longevity compared to state-of-the-art methods.