Emergency Caching: Coded Caching-based Reliable Map Transmission in Emergency Networks
This work addresses the need for effective data collection and processing in rescue missions, offering a domain-specific solution for emergency networks.
The paper tackles the problem of reliable map transmission in emergency networks by proposing a coded caching-based framework that uses UAVs to cache and collaboratively upload map fragments, resulting in improved transmission reliability compared to non-coding schemes as validated by simulation.
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.