Sarkar Mekhla, Sahoo Prasan Kumar
Department of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan 33302, Taiwan.
Department of Neurology, Chang Gung Memorial Hospital, Linkou Medical Center, Guishan, Taoyuan 333423, Taiwan.
Sensors (Basel). 2024 Aug 5;24(15):5076. doi: 10.3390/s24155076.
The rapid advancement of technology has greatly expanded the capabilities of unmanned aerial vehicles (UAVs) in wireless communication and edge computing domains. The primary objective of UAVs is the seamless transfer of video data streams to emergency responders. However, live video data streaming is inherently latency dependent, wherein the value of the video frames diminishes with any delay in the stream. This becomes particularly critical during emergencies, where live video streaming provides vital information about the current conditions. Edge computing seeks to address this latency issue in live video streaming by bringing computing resources closer to users. Nonetheless, the mobile nature of UAVs necessitates additional trajectory supervision alongside the management of computation and networking resources. Consequently, efficient system optimization is required to maximize the overall effectiveness of the collaborative system with limited UAV resources. This study explores a scenario where multiple UAVs collaborate with end users and edge servers to establish an emergency response system. The proposed idea takes a comprehensive approach by considering the entire emergency response system from the incident site to video distribution at the user level. It includes an adaptive resource management strategy, leveraging deep reinforcement learning by simultaneously addressing video streaming latency, UAV and user mobility factors, and varied bandwidth resources.
技术的飞速发展极大地扩展了无人机在无线通信和边缘计算领域的能力。无人机的主要目标是将视频数据流无缝传输给应急响应人员。然而，实时视频数据流本质上依赖于延迟，其中视频帧的价值会随着流中的任何延迟而降低。在紧急情况下，这一点变得尤为关键，因为实时视频流提供了有关当前状况的重要信息。边缘计算试图通过将计算资源靠近用户来解决实时视频流中的延迟问题。尽管如此，无人机的移动特性在计算和网络资源管理之外还需要额外的轨迹监控。因此，需要进行高效的系统优化，以在有限的无人机资源下最大化协作系统的整体效能。本研究探讨了一种多个无人机与终端用户和边缘服务器协作以建立应急响应系统的场景。所提出的想法采用了一种全面的方法，从事故现场到用户层面的视频分发，考虑了整个应急响应系统。它包括一种自适应资源管理策略，通过同时解决视频流延迟、无人机和用户移动性因素以及不同的带宽资源，利用深度强化学习。
