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arXiv · 2404.10845

Top-k Multi-Armed Bandit Learning for Content Dissemination in Swarms of Micro-UAVs

Amit Kumar Bhuyan·Hrishikesh Dutta·Subir Biswas
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Abstract

This paper presents a Micro-Unmanned Aerial Vehicle (UAV)-enhanced content management system for disaster scenarios where communication infrastructure is generally compromised. Utilizing a hybrid network of stationary and mobile Micro-UAVs, this system aims to provide crucial content access to isolated communities. In the developed architecture, stationary anchor UAVs, equipped with vertical and lateral links, serve users in individual disaster-affected communities. and mobile micro-ferrying UAVs, with enhanced mobility, extend coverage across multiple such communities. The primary goal is to devise a content dissemination system that dynamically learns caching policies to maximize content accessibility to users left without communication infrastructure. The core contribution is an adaptive content dissemination framework that employs a decentralized Top-k Multi-Armed Bandit learning approach for efficient UAV caching decisions. This approach accounts for geo-temporal variations in content popularity and diverse user demands. Additionally, a Selective Caching Algorithm is proposed to minimize redundant content copies by leveraging inter-UAV information sharing. Through functional verification and performance evaluation, the proposed framework demonstrates improved system performance and adaptability across varying network sizes, micro-UAV swarms, and content popularity distributions.

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Amit Kumar Bhuyan, Hrishikesh Dutta, Subir Biswas. 2024-04-16. Top-k Multi-Armed Bandit Learning for Content Dissemination in Swarms of Micro-UAVs. https://arxiv.org/abs/2404.10845

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