Xing Na, Zhang Ye, Wang Yuehai, Zhou Yang
School of Information, North China University of Technology, No. 5 Jinyuanzhuang Road, Beijing 100144, China.
Sensors (Basel). 2024 Jan 30;24(3):887. doi: 10.3390/s24030887.
Unmanned Aerial Vehicles (UAVs) have critical applications in various real-world scenarios, including mapping unknown environments, military reconnaissance, and post-disaster search and rescue. In these scenarios where communication infrastructure is missing, UAVs will form an ad hoc network and perform tasks in a distributed manner. To efficiently carry out tasks, each UAV must acquire and share global status information and data from neighbors. Meanwhile, UAVs frequently operate in extreme conditions, including storms, lightning, and mountainous areas, which significantly degrade the quality of wireless communication. Additionally, the mobility of UAVs leads to dynamic changes in network topology. Therefore, we propose a method that utilizes graph neural networks (GNN) to learn cooperative data dissemination. This method leverages the network topology relationship and enables UAVs to learn a decision policy based on local data structure, ensuring that all UAVs can recover global information. We train the policy using reinforcement learning that enhances the effectiveness of each transmission. After repeated simulations, the results validate the effectiveness and generalization of the proposed method.
无人机（UAV）在各种现实世界场景中都有重要应用，包括绘制未知环境地图、军事侦察和灾后搜索救援。在这些缺少通信基础设施的场景中，无人机将形成一个自组织网络并以分布式方式执行任务。为了高效执行任务，每架无人机必须获取并共享全局状态信息以及来自邻居的数据。同时，无人机经常在极端条件下运行，包括风暴、闪电和山区，这会显著降低无线通信质量。此外，无人机的移动性导致网络拓扑动态变化。因此，我们提出一种利用图神经网络（GNN）来学习协作数据传播的方法。该方法利用网络拓扑关系，使无人机能够基于局部数据结构学习决策策略，确保所有无人机都能恢复全局信息。我们使用强化学习训练该策略，以提高每次传输的有效性。经过反复模拟，结果验证了所提方法的有效性和通用性。
