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

2024-04-16

arXiv.org

AI Summary

To address dynamic, heterogeneous information demands in isolated communities following disaster-induced communication outages, this paper proposes a decentralized content caching optimization framework for hybrid UAV networks comprising static anchor nodes and mobile micro-carriers. The method introduces a novel geography- and time-aware Top-k multi-armed bandit (MAB) framework, integrating decentralized collaborative learning with selective caching to enable real-time popularity modeling and adaptive cache policy updates. Experimental evaluation across diverse network scales and content popularity distributions demonstrates that the approach improves content hit rate by 23.6% and reduces cache redundancy by 41.2%, significantly enhancing system robustness and responsiveness to rapid demand shifts. The framework establishes a scalable, low-overhead information dissemination paradigm tailored for emergency edge networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web

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.

Problem

• Disaster Communication
• Drone Swarms
• Information Dissemination Efficiency

Innovation

• Top-k Method
• Flexible Information Storage
• Efficient Information Propagation