Multi-Armed Bandit Learning for Content Provisioning in Network of UAVs
Amit Kumar Bhuyan Hrishikesh Dutta Subir Biswas
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.