Emergency Computing: An Adaptive Collaborative Inference Method Based on
  Hierarchical Reinforcement Learning
Weiqi Fu Lianming Xu Xin Wu Li Wang Aiguo Fei
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.