In achieving effective emergency response, the timely acquisition of environmental information, seamless command data transmission, and prompt decision-making are crucial. This necessitates the establishment of a resilient emergency communication dedicated network, capable of providing communication and sensing services even in the absence of basic infrastructure. In this paper, we propose an Emergency Network with Sensing, Communication, Computation, Caching, and Intelligence (E-SC3I). The framework incorporates mechanisms for emergency computing, caching, integrated communication and sensing, and intelligence empowerment. E-SC3I ensures rapid access to a large user base, reliable data transmission over unstable links, and dynamic network deployment in a changing environment. However, these advantages come at the cost of significant computation overhead. Therefore, we specifically concentrate on emergency computing and propose an adaptive collaborative inference method (ACIM) based on hierarchical reinforcement learning. Experimental results demonstrate our method's ability to achieve rapid inference of AI models with constrained computational and communication resources.

翻译：在实现有效应急响应的过程中，及时获取环境信息、无缝传输指挥数据以及快速做出决策至关重要。这需要建立一个具有韧性的应急通信专网，使其即便在缺乏基本基础设施的条件下，也能提供通信和感知服务。本文提出了一种具备感知、通信、计算、缓存与智能能力的应急网络（E-SC3I）。该框架融合了应急计算、缓存、通感一体化及智能赋能机制。E-SC3I可确保在用户数量庞大时实现快速接入，在不稳定链路上实现可靠数据传输，并在动态环境中实现网络灵活部署。然而，这些优势是以巨大的计算开销为代价的。因此，我们重点聚焦于应急计算，并提出了一种基于分层强化学习的自适应协同推理方法（ACIM）。实验结果表明，该方法能够在计算和通信资源受限的条件下，实现AI模型的快速推理。