This paper proposes an unmanned aerial vehicle (UAV) aided content management
system in communication-challenged disaster scenarios. Without cellular
infrastructure in such scenarios, community of stranded users can be provided
access to situation-critical contents using a hybrid network of static and
traveling UAVs. A set of relatively static anchor UAVs can download content
from central servers and provide content access to its local users. A set of
ferrying UAVs with wider mobility can provision content to users by shuffling
them across different anchor UAVs while visiting different communities of
users. The objective is to design a content dissemination system that
on-the-fly learns content caching policies for maximizing content availability
to the stranded users. This paper proposes a decentralized Top-k Multi-Armed
Bandit Learning model for UAV-caching decision-making that takes geo-temporal
differences in content popularity and heterogeneity in content demands into
consideration. The proposed paradigm is able to combine the expected reward
maximization attribute and a proposed multi-dimensional reward structure of
Top-k Multi-Armed Bandit, for caching decision at the UAVs. This study is done
for different user-specified tolerable access delay, heterogeneous popularity
distributions, and inter-community geographical characteristics. Functional
verification and performance evaluation of the proposed caching framework is
done for a wide range of network size, UAV distribution, and content
popularity.

本文提出了一种在通信困难的灾害情景中利用无人机辅助的内容管理系统，通过静态和移动无人机的混合网络，给被困用户提供关键信息的访问。为了最大化被困用户的内容可用性，设计了一种实时学习内容缓存策略的内容传播系统。通过使用去中心化的多臂赌博学习模型，考虑了地理和时间上的差异，以及内容的异质性需求，实现了无人机缓存决策的优化。通过各种网络规模、无人机分布和内容受欢迎程度的功能验证和性能评估，验证了所提出的缓存框架的有效性。