Top-k Multi-Armed Bandit Learning for Content Dissemination in Swarms of Micro-UAVs
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909457317888000 |
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| author | Bhuyan, Amit Kumar Dutta, Hrishikesh Biswas, Subir |
| author_facet | Bhuyan, Amit Kumar Dutta, Hrishikesh Biswas, Subir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10845 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Top-k Multi-Armed Bandit Learning for Content Dissemination in Swarms of Micro-UAVs Bhuyan, Amit Kumar Dutta, Hrishikesh Biswas, Subir Machine Learning Networking and Internet Architecture I.2.11 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. |
| title | Top-k Multi-Armed Bandit Learning for Content Dissemination in Swarms of Micro-UAVs |
| topic | Machine Learning Networking and Internet Architecture I.2.11 |
| url | https://arxiv.org/abs/2404.10845 |