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Resource Optimization in UAV-assisted IoT Networks: The Role of Generative AI

Sana Sharif, Sherali Zeadally, Waleed Ejaz

TL;DR

This paper investigates how generative AI can optimize resources in UAV-assisted IoT networks, focusing on real-time decision-making for dynamic environments and public-safety use cases. It reviews key generative AI models—GANs, VAEs, flow-based models, and LLMs—and discusses their roles in data augmentation, distribution learning, and decision support for resource allocation and trajectory planning. A public-safety use case demonstrates progressive integration: rule-based optimization, AI-driven resource allocation, and GAN–LLM collaboration to enhance handovers, situational awareness, and data-driven decision-making. The work also outlines challenges in computational efficiency, scalability, robustness, ecosystem integration, and regulatory compliance, offering directions for future research toward more intelligent, adaptive, and secure UAV-enabled IoT systems.

Abstract

We investigate how generative Artificial Intelligence (AI) can be used to optimize resources in Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) networks. In particular, generative AI models for real-time decision-making have been used in public safety scenarios. This work describes how generative AI models can improve resource management within UAV-assisted networks. Furthermore, this work presents generative AI in UAV-assisted networks to demonstrate its practical applications and highlight its broader capabilities. We demonstrate a real-life case study for public safety, demonstrating how generative AI can enhance real-time decision-making and improve training datasets. By leveraging generative AI in UAV- assisted networks, we can design more intelligent, adaptive, and efficient ecosystems to meet the evolving demands of wireless networks and diverse applications. Finally, we discuss challenges and future research directions associated with generative AI for resource optimization in UAV-assisted networks.

Resource Optimization in UAV-assisted IoT Networks: The Role of Generative AI

TL;DR

This paper investigates how generative AI can optimize resources in UAV-assisted IoT networks, focusing on real-time decision-making for dynamic environments and public-safety use cases. It reviews key generative AI models—GANs, VAEs, flow-based models, and LLMs—and discusses their roles in data augmentation, distribution learning, and decision support for resource allocation and trajectory planning. A public-safety use case demonstrates progressive integration: rule-based optimization, AI-driven resource allocation, and GAN–LLM collaboration to enhance handovers, situational awareness, and data-driven decision-making. The work also outlines challenges in computational efficiency, scalability, robustness, ecosystem integration, and regulatory compliance, offering directions for future research toward more intelligent, adaptive, and secure UAV-enabled IoT systems.

Abstract

We investigate how generative Artificial Intelligence (AI) can be used to optimize resources in Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) networks. In particular, generative AI models for real-time decision-making have been used in public safety scenarios. This work describes how generative AI models can improve resource management within UAV-assisted networks. Furthermore, this work presents generative AI in UAV-assisted networks to demonstrate its practical applications and highlight its broader capabilities. We demonstrate a real-life case study for public safety, demonstrating how generative AI can enhance real-time decision-making and improve training datasets. By leveraging generative AI in UAV- assisted networks, we can design more intelligent, adaptive, and efficient ecosystems to meet the evolving demands of wireless networks and diverse applications. Finally, we discuss challenges and future research directions associated with generative AI for resource optimization in UAV-assisted networks.
Paper Structure (18 sections, 4 figures, 1 table, 1 algorithm)

This paper contains 18 sections, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Generative AI Models
  3. Generative Adversarial Networks (GANs)
  4. Variational Autoencoders (VAEs)
  5. Flow Based Models
  6. Large Language Models (LLMs)
  7. Related Works
  8. Use Case: Public Safety
  9. Rule-Based Optimization: Traditional Strategies for Resource Management
  10. AI-Powered Resource Allocation: Leveraging Real-Time Data for Public Safety
  11. GAN and LLM Integration: Revolutionizing Resource Management in Public Safety
  12. Challenges, Future Research Directions and Recommendations
  13. Optimizing computational efficiency
  14. Adaptable scaling and efficiency
  15. Enhancing robustness and reliability
  16. ...and 3 more sections

Figures (4)

  • Figure 1: AI and Generative AI models. GAN: Generative Adversarial Networks; VAEs: Variational AutoEncoders; LLMs: Large Language Models.
  • Figure 2: Organization of the paper.
  • Figure 3: Generative AI models (a) Generative adversarial networks, (b) Variational autoencoder, (c) Flow based model, and (d) Large language model.
  • Figure 4: Generative AI for UAV-assisted IoT networks in public safety.