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This paper addresses the task offloading problem in Mobile Edge Computing (MEC) enabled Vehicular Medical (MVM) networks, where stringent requirements for service quality and low latency are critical due to the time-sensitive nature of medical applications. To cope with dynamic task requests, we formulate a joint optimization model to maximize task success rate while minimizing processing delay. A Task Offloading (PTO) mechanism based on Proximal Policy Optimization (PPO) algorithm is proposed to enable each vehicle to make intelligent offloading decisions by only using the local observations. Simulation results show that the PTO mechanism outperforms baseline methods by reducing task dropped ratio and task delay.

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Proximal Policy Optimization Based Task Offloading Optimization in MEC-enabled Vehicular Medical Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Siquan Liu,
  • Chuangchuang Zhang,
  • Hongyong Yang,
  • Kunqi Lv

摘要

This paper addresses the task offloading problem in Mobile Edge Computing (MEC) enabled Vehicular Medical (MVM) networks, where stringent requirements for service quality and low latency are critical due to the time-sensitive nature of medical applications. To cope with dynamic task requests, we formulate a joint optimization model to maximize task success rate while minimizing processing delay. A Task Offloading (PTO) mechanism based on Proximal Policy Optimization (PPO) algorithm is proposed to enable each vehicle to make intelligent offloading decisions by only using the local observations. Simulation results show that the PTO mechanism outperforms baseline methods by reducing task dropped ratio and task delay.