Top-k Multi-Armed Bandit Learning for Content Dissemination in Swarms of
  Micro-UAVs
Amit Kumar Bhuyan Hrishikesh Dutta Subir Biswas
In communication-deprived disaster scenarios, this paper introduces a
Micro-Unmanned Aerial Vehicle (UAV)- enhanced content management system. In the
absence of cellular infrastructure, this system deploys a hybrid network of
stationary and mobile UAVs to offer vital content access to isolated
communities. Static anchor UAVs equipped with both vertical and lateral links
cater to local users, while agile micro-ferrying UAVs, equipped with lateral
links and greater mobility, reach users in various communities. The primary
goal is to devise an adaptive content dissemination system that dynamically
learns caching policies to maximize content accessibility. The paper proposes a
decentralized Top-k Multi-Armed Bandit (Top-k MAB) learning approach for UAV
caching decisions, accommodating geotemporal disparities in content popularity
and diverse content demands. The proposed mechanism involves a Selective
Caching Algorithm that algorithmically reduces redundant copies of the contents
by leveraging the shared information between the UAVs. It is demonstrated that
Top-k MAB learning, along with selective caching algorithm, can improve system
performance while making the learning process adaptive. The paper does
functional verification and performance evaluation of the proposed caching
framework under a wide range of network size, swarm of micro-ferrying UAVs, and
heterogeneous popularity distributions.