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

• URL: http://arxiv.org/abs/2402.02146v1
• Date: Sat, 3 Feb 2024 13:28:35 GMT
• Title: Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learning
• Authors: Weiqi Fu, Lianming Xu, Xin Wu, Li Wang, Aiguo Fei
• Abstract summary: 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. We specifically concentrate on emergency computing and propose an adaptive collaborative inference method (ACIM) based on hierarchical reinforcement learning.
• Score: 14.929735103723573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• AI Flow at the Network Edge [58.31090055138711]
  AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers. This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
  arXiv  Detail & Related papers  (2024-11-19T12:51:17Z)
• Hypergame Theory for Decentralized Resource Allocation in Multi-user Semantic Communications [60.63472821600567]
  A novel framework for decentralized computing and communication resource allocation in multiuser SC systems is proposed. The challenge of efficiently allocating communication and computing resources is addressed through the application of Stackelberg hyper game theory. Simulation results show that the proposed Stackelberg hyper game results in efficient usage of communication and computing resources.
  arXiv  Detail & Related papers  (2024-09-26T15:55:59Z)
• RIS-Based On-the-Air Semantic Communications -- a Diffractional Deep Neural Network Approach [10.626169088908867]
  Current AI-based semantic communication methods require digital hardware for implementation. RIS-based semantic communications offer appealing features, such as light-speed computation, low computational power requirements, and the ability to handle multiple tasks simultaneously.
  arXiv  Detail & Related papers  (2023-12-01T12:15:49Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• Federated Reinforcement Learning at the Edge [1.4271989597349055]
  Modern cyber-physical architectures use data collected from systems at different physical locations to learn appropriate behaviors and adapt to uncertain environments. This paper considers a setup where multiple agents need to communicate efficiently in order to jointly solve a reinforcement learning problem over time-series data collected in a distributed manner. An algorithm for achieving communication efficiency is proposed, supported with theoretical guarantees, practical implementations, and numerical evaluations.
  arXiv  Detail & Related papers  (2021-12-11T03:28:59Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services [10.718353079920007]
  This paper presents a novel architecture that leverages Deep Reinforcement Learning at the edge of the network in order to address Radio Access Network Slicing and Radio Resource Management. The effectiveness of our proposal against baseline methodologies is investigated through computer simulation, by considering an autonomous-driving use-case.
  arXiv  Detail & Related papers  (2021-03-18T14:18:34Z)
• Linear Regression over Networks with Communication Guarantees [1.4271989597349055]
  In connected autonomous systems, data transfer takes place over communication networks with often limited resources. This paper examines algorithms for communication-efficient learning for linear regression tasks by exploiting the informativeness of the data.
  arXiv  Detail & Related papers  (2021-03-06T15:28:21Z)
• Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots [58.980293789967575]
  A communication enabled indoor intelligent robots (IRs) service framework is proposed. Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent.
  arXiv  Detail & Related papers  (2020-11-23T21:45:01Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.