北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (5): 66-73.

• 论文 • 上一篇    下一篇

基于AoI的多无人机物联网任务分配和轨迹规划

周子轩,李新凯,张宏立   

  1. 新疆大学
  • 收稿日期:2023-08-24 修回日期:2023-09-18 出版日期:2024-10-28 发布日期:2024-11-10
  • 通讯作者: 李新凯 E-mail:lxk@xju.edu.cn
  • 基金资助:
    国家自然科学基金;新疆维吾尔自治区自然科学基金青年科学基金

AoI based multi-UAV iot task assignment and trajectory planning

  • Received:2023-08-24 Revised:2023-09-18 Online:2024-10-28 Published:2024-11-10

摘要: 信息年龄(Age of Information,AoI)描述了自信息发出到接收所经过的时间,能够对数据的价值做出准确的衡量。针对应急通信中的平均AoI最小化问题,引入无人机作为信息中继并提出了基于深度强化学习框架的任务分配和轨迹优化算法。首先分析并证明了原始AoI最小化问题与无人机辅助应急通信之间的联系,并将AoI最小化问题分成两个阶段去求解。其次,为了减少无人机无效的飞行时间,制定了新的规划策略,通过将沿途的受灾节点纳入轨迹中,使轨迹更加平滑。最后,为了避免了无人机重复访问同一个救援小组,设计了一种集中的信息共享机制,节省了能耗和信息分发时间。实验结果表明,相比于传统优化算法,本文所提出的优化算法能够实现更小的信息年龄。

关键词: 无人机辅助物联网, 信息年龄, 任务分配, 路径规划, 深度强化学习

Abstract: Age of Information (AoI) describes the time from the time when information is sent to the time when it is received, which can accurately measure the value of data. In order to minimize the average AoI in emergency communication, unmanned aerial vehicles (UAVs) are introduced as information relay and a task assignment and trajectory optimization algorithm based on deep reinforcement learning framework is proposed. Firstly, the relationship between the original AoI minimization problem and UAV-assisted emergency communication is analyzed and proved, and the AoI minimization problem is divided into two stages to solve. Secondly, in order to reduce the ineffective flight time of drones, new planning strategies have been developed to make the trajectory smoother by incorporating disaster nodes along the way into the trajectory. Finally, in order to avoid the UAV repeatedly visiting the same rescue group, a centralized information sharing mechanism is designed to save energy consumption and information distribution time. Experimental results show that compared with traditional optimization algorithms, the proposed optimization algorithm can achieve a smaller information age.

Key words: UAV-assisted Internet of Things, Age of Information, task assignment, trajectory optimization, Deep reinforcement learning

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