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.

翻译：无人机辅助传感网络（UASNets）在开拓新机遇方面发挥着关键作用，其全球民用应用正经历显著增长。UASNets通过及时监控改进灾害管理，并借助精细作物监测推进精准农业，从而显著变革商业经济。通过提供更高的效率、安全性和成本效益，UASNets彻底改变了商业领域，彰显其变革性影响。这些新能力与变革的核心在于从崎岖偏远地区采集数据。凭借卓越机动性与可操控性，无人机被用于在严苛环境中（如自然灾害监测、边境监控和应急响应监测）从地面传感器采集数据。此类场景中的主要挑战是：无人机的移动会影响信道条件并导致数据包丢失。无人机快速移动会造成信道条件恶化及信号快速衰减，进而引发丢包；反之，若无人机移动缓慢，则可能导致地面传感器缓冲溢出，因新到达的数据未被及时采集。为应对该挑战，我们提出通过联合优化多架无人机的速度控制与数据采集调度来最小化丢包率。此外，在UASNets中，无人机快速移动会导致信道条件恶劣及信号快速衰减，从而延长信息年龄（AoI）；而无人机缓慢移动则会延长飞行时间，进而增加地面传感器的AoI。针对此问题，我们提出一种新的均值场飞行资源分配优化方案，以最小化感知数据的AoI。