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LLMs are everywhere: Ubiquitous Utilization of AI Models through Air Computing

Baris Yamansavascilar, Atay Ozgovde, Cem Ersoy

TL;DR

This work examines ubiquitous execution of LLMs via air computing, a 3D networking paradigm that augments edge resources with layered aerial platforms (LAP, HAP, LEO) to sustain AI-powered services. It analyzes core LLM capabilities—reasoning, in-context learning, and generalization—and how air computing enables dynamic task offloading, caching, and coverage across diverse environments. Through use cases in outdoor activities, remote health, sports/concert events, and a disaster-response case study, the paper demonstrates that airborne LLMs can improve task success rates and QoS/QoE when terrestrial infrastructure is compromised. The findings highlight practical implications for scalable, resilient AI-enabled services across edge and aerial networks, with potential to enhance emergency response and public safety.

Abstract

We are witnessing a new era where problem-solving and cognitive tasks are being increasingly delegated to Large Language Models (LLMs) across diverse domains, ranging from code generation to holiday planning. This trend also creates a demand for the ubiquitous execution of LLM-powered applications in a wide variety of environments in which traditional terrestrial 2D networking infrastructures may prove insufficient. A promising solution in this context is to extend edge computing into a 3D setting to include aerial platforms organized in multiple layers, a paradigm we refer to as air computing, to augment local devices for running LLM and Generative AI (GenAI) applications. This approach alleviates the strain on existing infrastructure while enhancing service efficiency by offloading computational tasks to the corresponding air units such as UAVs. Furthermore, the coordinated deployment of various air units can significantly improve the Quality of Experience (QoE) by ensuring seamless, adaptive, and resilient task execution. In this study, we investigate the synergy between LLM-based applications and air computing, exploring their potential across various use cases. Additionally, we present a disaster response case study demonstrating how the collaborative utilization of LLMs and air computing can significantly improve outcomes in critical situations.

LLMs are everywhere: Ubiquitous Utilization of AI Models through Air Computing

TL;DR

This work examines ubiquitous execution of LLMs via air computing, a 3D networking paradigm that augments edge resources with layered aerial platforms (LAP, HAP, LEO) to sustain AI-powered services. It analyzes core LLM capabilities—reasoning, in-context learning, and generalization—and how air computing enables dynamic task offloading, caching, and coverage across diverse environments. Through use cases in outdoor activities, remote health, sports/concert events, and a disaster-response case study, the paper demonstrates that airborne LLMs can improve task success rates and QoS/QoE when terrestrial infrastructure is compromised. The findings highlight practical implications for scalable, resilient AI-enabled services across edge and aerial networks, with potential to enhance emergency response and public safety.

Abstract

We are witnessing a new era where problem-solving and cognitive tasks are being increasingly delegated to Large Language Models (LLMs) across diverse domains, ranging from code generation to holiday planning. This trend also creates a demand for the ubiquitous execution of LLM-powered applications in a wide variety of environments in which traditional terrestrial 2D networking infrastructures may prove insufficient. A promising solution in this context is to extend edge computing into a 3D setting to include aerial platforms organized in multiple layers, a paradigm we refer to as air computing, to augment local devices for running LLM and Generative AI (GenAI) applications. This approach alleviates the strain on existing infrastructure while enhancing service efficiency by offloading computational tasks to the corresponding air units such as UAVs. Furthermore, the coordinated deployment of various air units can significantly improve the Quality of Experience (QoE) by ensuring seamless, adaptive, and resilient task execution. In this study, we investigate the synergy between LLM-based applications and air computing, exploring their potential across various use cases. Additionally, we present a disaster response case study demonstrating how the collaborative utilization of LLMs and air computing can significantly improve outcomes in critical situations.

Paper Structure

This paper contains 20 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Essential LLM Features
  4. Reasoning
  5. In-context Learning
  6. Generalization Ability
  7. Air Computing
  8. LAP
  9. HAP
  10. LEO
  11. Use Cases
  12. Outdoor Activities
  13. Remote Health
  14. Sports and Concert Activities
  15. Natural Disasters
  16. ...and 5 more sections

Figures (4)

  • Figure 1: An air Computing Environment
  • Figure 2: LLMs have generalization abilities so that they can process various task types with high performance.
  • Figure 3: The collaborative utilization of LLMs and air computing can provide innovative solutions for various use cases.
  • Figure 4: Disaster response evaluated with and without air computing support