Multi-Armed Bandit Learning for Content Provisioning in Network of UAVs
- URL: http://arxiv.org/abs/2312.14967v1
- Date: Mon, 18 Dec 2023 15:24:01 GMT
- Title: Multi-Armed Bandit Learning for Content Provisioning in Network of UAVs
- Authors: Amit Kumar Bhuyan, Hrishikesh Dutta, and Subir Biswas
- Abstract summary: 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.
- Score: 2.3076690318595676
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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