Many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing. In this study, we propose a three-layer architecture of emergency caching networks focusing on data collection and reliable transmission, by leveraging efficient perception and edge caching technologies. Based on this architecture, we propose a disaster map collection framework that integrates coded caching technologies. Our framework strategically caches coded fragments of maps across unmanned aerial vehicles (UAVs), fostering collaborative uploading for augmented transmission reliability. Additionally, we establish a comprehensive probability model to assess the effective recovery area of disaster maps. Towards the goal of utility maximization, we propose a deep reinforcement learning (DRL) based algorithm that jointly makes decisions about cooperative UAVs selection, bandwidth allocation and coded caching parameter adjustment, accommodating the real-time map updates in a dynamic disaster situation. Our proposed scheme is more effective than the non-coding caching scheme, as validated by simulation.

翻译：许多救援任务需要有效的感知和实时决策，这高度依赖于高效的数据收集与处理。本研究通过利用高效的感知和边缘缓存技术，提出了一种面向数据收集与可靠传输的三层应急缓存网络架构。基于该架构，我们设计了一个融合编码缓存技术的灾害地图收集框架。该框架策略性地将地图的编码片段缓存至无人机（UAV）上，通过协同上传增强传输可靠性。此外，我们建立了一个概率模型来评估灾害地图的有效恢复区域。为实现效用最大化，我们提出了一种基于深度强化学习（DRL）的算法，该算法联合决策协作无人机的选择、带宽分配与编码缓存参数调整，以适应动态灾害场景下地图的实时更新。仿真验证表明，所提方案比非编码缓存方案更有效。