Emergency Caching: Coded Caching-based Reliable Map Transmission in Emergency Networks
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913245474848768 |
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| author | Tian, Zeyu Xu, Lianming Li, Liang Wang, Li Fei, Aiguo |
| author_facet | Tian, Zeyu Xu, Lianming Li, Liang Wang, Li Fei, Aiguo |
| contents | Many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing. In this study, we propose a three-layer architecture of emergency caching networks focusing on data collection and reliable transmission, by leveraging efficient perception and edge caching technologies. Based on this architecture, we propose a disaster map collection framework that integrates coded caching technologies. Our framework strategically caches coded fragments of maps across unmanned aerial vehicles (UAVs), fostering collaborative uploading for augmented transmission reliability. Additionally, we establish a comprehensive probability model to assess the effective recovery area of disaster maps. Towards the goal of utility maximization, we propose a deep reinforcement learning (DRL) based algorithm that jointly makes decisions about cooperative UAVs selection, bandwidth allocation and coded caching parameter adjustment, accommodating the real-time map updates in a dynamic disaster situation. Our proposed scheme is more effective than the non-coding caching scheme, as validated by simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17550 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emergency Caching: Coded Caching-based Reliable Map Transmission in Emergency Networks Tian, Zeyu Xu, Lianming Li, Liang Wang, Li Fei, Aiguo Networking and Internet Architecture Artificial Intelligence Signal Processing Many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing. In this study, we propose a three-layer architecture of emergency caching networks focusing on data collection and reliable transmission, by leveraging efficient perception and edge caching technologies. Based on this architecture, we propose a disaster map collection framework that integrates coded caching technologies. Our framework strategically caches coded fragments of maps across unmanned aerial vehicles (UAVs), fostering collaborative uploading for augmented transmission reliability. Additionally, we establish a comprehensive probability model to assess the effective recovery area of disaster maps. Towards the goal of utility maximization, we propose a deep reinforcement learning (DRL) based algorithm that jointly makes decisions about cooperative UAVs selection, bandwidth allocation and coded caching parameter adjustment, accommodating the real-time map updates in a dynamic disaster situation. Our proposed scheme is more effective than the non-coding caching scheme, as validated by simulation. |
| title | Emergency Caching: Coded Caching-based Reliable Map Transmission in Emergency Networks |
| topic | Networking and Internet Architecture Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2402.17550 |