Resource Optimization in UAV-Assisted IoT Networks: The Role of Generative AI
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 dis-cuss challenges and future research directions associated with generative AI for resource optimization in UAV-assisted networks.
Authors
- Sherali Zeadally (ORCID: https://orcid.org/0000-0002-5982-8190)
- Waleed Ejaz (ORCID: https://orcid.org/0000-0002-6289-1406)
- Sana Sharif
Institutions
- University of Kentucky (US)
- Lakehead University (CA)
Publication Details
- Journal
- IEEE Internet of Things Magazine
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1109/iotm.001.2400045
- Citations
- 2
- Primary Topic
- IoT and Edge/Fog Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.70