# Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learning

arXiv: https://arxiv.org/abs/2402.02146 · HTML: https://mlsanity.com/paper/2402.02146/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 6 of 6)

- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://mlsanity.com/paper/1802.03494/) (2018)
- [Communication-Efficient Edge AI: Algorithms and Systems](https://mlsanity.com/paper/2002.09668/) (2020)
- [BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems](https://mlsanity.com/paper/1910.14315/) (2019)
- [Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms](https://mlsanity.com/paper/1802.03835/) (2018)
- [Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning](https://mlsanity.com/paper/1903.03472/) (2019)

## Abstract (arXiv)

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.