无人机辅助传感器网络中联合巡航控制与智能数据采集的深度强化学习方法
Deep Reinforcement Learning for Joint Cruise Control and Intelligent Data Acquisition in UAVs-Assisted Sensor Networks
AI总结：
针对无人机辅助传感器网络中无人机移动速度引发的丢包与信息年龄过高问题，提出联合优化多无人机速度与采集调度的丢包最小化方案，以及平均场飞行资源分配优化方法以降低信息年龄。
摘要：
无人机辅助传感器网络（UASNets）正凭借其创造的全新机遇在全球民用应用中实现迅猛发展，发挥着至关重要的作用。UASNets通过及时监测优化灾害管理工作，借助精细化作物监测推动精准农业发展，进而显著变革商业经济格局。UASNets凭借更高的效率、安全性与成本效益革新商业领域，凸显出其颠覆性的影响。这些全新能力与变革的核心基础之一，是从地形崎岖、位置偏远的区域采集数据。由于无人机具备出色的移动性与机动性，被应用于自然灾害监测、边境巡逻、应急响应监测等恶劣场景中，从地面传感器采集数据。这类场景面临的一大核心挑战是：无人机的移动会影响信道状态，进而导致丢包。无人机高速移动会造成信道状态恶化、信号快速衰减，最终引发丢包；另一方面，无人机移动速度过慢则会导致地面传感器缓冲区溢出，因为新生成的数据无法被无人机及时采集。\n 针对这一挑战，我们提出的方案是通过联合优化多架无人机的速度控制与数据采集调度，实现丢包最小化。此外，在UASNets中，无人机的快速移动会导致信道状态变差、信号快速衰减，进而延长信息年龄（AoI）；与之相对，无人机移动速度过慢会延长飞行时间，同样会增加地面传感器的信息年龄。针对这一挑战，我们提出了一种全新的平均场飞行资源分配优化方法，以最小化传感数据的信息年龄。
英文摘要：
Unmanned aerial vehicle (UAV)-assisted sensor networks (UASNets), which play a crucial role in creating new opportunities, are experiencing significant growth in civil applications worldwide. UASNets improve disaster management through timely surveillance and advance precision agriculture with detailed crop monitoring, thereby significantly transforming the commercial economy. UASNets revolutionize the commercial sector by offering greater efficiency, safety, and cost-effectiveness, highlighting their transformative impact. A fundamental aspect of these new capabilities and changes is the collection of data from rugged and remote areas. Due to their excellent mobility and maneuverability, UAVs are employed to collect data from ground sensors in harsh environments, such as natural disaster monitoring, border surveillance, and emergency response monitoring. One major challenge in these scenarios is that the movements of UAVs affect channel conditions and result in packet loss. Fast movements of UAVs lead to poor channel conditions and rapid signal degradation, resulting in packet loss. On the other hand, slow mobility of a UAV can cause buffer overflows of the ground sensors, as newly arrived data is not promptly collected by the UAV. Our proposal to address this challenge is to minimize packet loss by jointly optimizing the velocity controls and data collection schedules of multiple UAVs.Furthermore, in UASNets, swift movements of UAVs result in poor channel conditions and fast signal attenuation, leading to an extended age of information (AoI). In contrast, slow movements of UAVs prolong flight time, thereby extending the AoI of ground sensors.To address this challenge, we propose a new mean-field flight resource allocation optimization to minimize the AoI of sensory data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
