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.

为了实现有效的应急响应，在缺乏基础设施的情况下，及时获取环境信息、无缝的命令数据传输和快速决策至关重要。本文提出了一个应急网络框架，包括应急计算、缓存、集成通信与感知以及智能增强等机制。该框架可确保对大型用户群体的快速访问，通过不稳定链路进行可靠的数据传输，以及在不断变化的环境下进行动态网络部署。然而，这些优势的实现需要付出大量的计算开销。因此，我们专门研究了应急计算，并提出了一种基于分层强化学习的自适应协作推断方法（ACIM）。实验结果表明，我们的方法能够在受限的计算和通信资源下实现对人工智能模型的快速推断。

应急计算：一种基于分层强化学习的自适应协作推理方法