Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learning
This work addresses the problem of timely decision-making in emergency response scenarios, representing an incremental improvement in optimizing resource usage for AI tasks in such environments.
The paper tackles the challenge of enabling rapid AI inference in emergency networks with limited computational and communication resources, proposing an adaptive collaborative inference method based on hierarchical reinforcement learning that achieves fast inference under these constraints.
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.