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<p>During the COVID-19 pandemic, effective monitoring and prediction of the evolution of the epidemic are crucial for public health safety. However, traditional monitoring methods exhibit significant shortcomings in data collection efficiency and energy consumption management. In response to this challenge, this research explores the energy consumption issue of mobile edge devices monitoring residents’ health data in densely populated urban centers, aiming to minimize the energy consumption and charging time of edge devices while ensuring comprehensive and efficient collection of individual health data. To achieve this goal, this paper proposes a novel optimization Firefly algorithm based on the Deep Q-Network (DQN_FA), which is used to optimize the deployment strategy of mobile edge devices. Through simulation experiments, we compare five mobile edge device deployment strategies based on different swarm intelligence optimization algorithms. The experimental results show that the DQN_FA algorithm exhibits comprehensive advantages in performance compared to other intelligent optimization algorithms.</p>

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Energy-aware mobile edge device deployment in health data detection scenario: a DQN optimized firefly algorithm

  • 期刊论文

  • 发表日期：

    2025年4月19日

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  • Shanshan He,
  • Huixi Li,
  • Yuelei Ji,
  • Xingjian Chen,
  • Feifei Zou,
  • Xia Zhao

摘要

During the COVID-19 pandemic, effective monitoring and prediction of the evolution of the epidemic are crucial for public health safety. However, traditional monitoring methods exhibit significant shortcomings in data collection efficiency and energy consumption management. In response to this challenge, this research explores the energy consumption issue of mobile edge devices monitoring residents’ health data in densely populated urban centers, aiming to minimize the energy consumption and charging time of edge devices while ensuring comprehensive and efficient collection of individual health data. To achieve this goal, this paper proposes a novel optimization Firefly algorithm based on the Deep Q-Network (DQN_FA), which is used to optimize the deployment strategy of mobile edge devices. Through simulation experiments, we compare five mobile edge device deployment strategies based on different swarm intelligence optimization algorithms. The experimental results show that the DQN_FA algorithm exhibits comprehensive advantages in performance compared to other intelligent optimization algorithms.