Scholarlink

<p>The rapid growth of connected medical devices generates massive volumes of heterogeneous health data that must be processed and transmitted in real time. In such environments, minimizing latency and energy consumption remains a critical challenge for next-generation health monitoring systems. Existing reinforcement learning and optimization methods for intelligent communication networks face several challenges, including slow convergence, high computational overhead, and inefficiency in handling task prioritization. To resolve these issues, this work develops a chaotic dung beetle optimization-boosted multi-agent deep reinforcement learning that jointly optimizes communication reliability, computational efficiency, and task prioritization. A reward function is designed to jointly minimize delay, energy usage, and system cost while preserving information freshness. Specifically, the dung beetle optimization process is combined with a piecewise linear chaotic map to enhance population diversity, which significantly improves search space exploration and leads to faster convergence and higher solution quality. The proposed algorithm enhances the exploration capability of multi-agent deep reinforcement learning through the integration of chaotic dung beetle optimization, enabling more accurate and reliable decision-making in real-world applications. Extensive experiments demonstrate that the proposed chaotic dung beetle optimization-boosted multi-agent deep reinforcement learning model achieves superior performance compared to baseline algorithms. Specifically, it reaches an accuracy of over 97.00% with rapid convergence, reduces system cost under varying health data sizes and Medical Internet of Things devices, and maintains robust scalability across diverse workloads. Moreover, the model achieves significant reductions in communication latency and energy consumption as central processing unit cycles and bandwidth increase, while effectively prioritizing high-criticality tasks.</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

Chaotic dung beetle optimization–enhanced multi-agent deep reinforcement learning for joint task offloading and resource allocation in multi-unmanned aerial vehicle internet of medical things networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月17日

查看全文
  • Gauri Kalnoor,
  • Vijayalaxmi Kadrolli

摘要

The rapid growth of connected medical devices generates massive volumes of heterogeneous health data that must be processed and transmitted in real time. In such environments, minimizing latency and energy consumption remains a critical challenge for next-generation health monitoring systems. Existing reinforcement learning and optimization methods for intelligent communication networks face several challenges, including slow convergence, high computational overhead, and inefficiency in handling task prioritization. To resolve these issues, this work develops a chaotic dung beetle optimization-boosted multi-agent deep reinforcement learning that jointly optimizes communication reliability, computational efficiency, and task prioritization. A reward function is designed to jointly minimize delay, energy usage, and system cost while preserving information freshness. Specifically, the dung beetle optimization process is combined with a piecewise linear chaotic map to enhance population diversity, which significantly improves search space exploration and leads to faster convergence and higher solution quality. The proposed algorithm enhances the exploration capability of multi-agent deep reinforcement learning through the integration of chaotic dung beetle optimization, enabling more accurate and reliable decision-making in real-world applications. Extensive experiments demonstrate that the proposed chaotic dung beetle optimization-boosted multi-agent deep reinforcement learning model achieves superior performance compared to baseline algorithms. Specifically, it reaches an accuracy of over 97.00% with rapid convergence, reduces system cost under varying health data sizes and Medical Internet of Things devices, and maintains robust scalability across diverse workloads. Moreover, the model achieves significant reductions in communication latency and energy consumption as central processing unit cycles and bandwidth increase, while effectively prioritizing high-criticality tasks.