# FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoring

arXiv: https://arxiv.org/abs/2507.10134 · HTML: https://mlsanity.com/paper/2507.10134/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities](https://mlsanity.com/paper/2309.16739/) (2023)
- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends](https://mlsanity.com/paper/2409.14457/) (2024)
- [Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control](https://mlsanity.com/paper/2408.00214/) (2024)
- [Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection](https://mlsanity.com/paper/2405.11002/) (2024)
- [Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities](https://mlsanity.com/paper/2402.18062/) (2024)
- [Large Language Models for Wireless Networks: An Overview from the Prompt Engineering Perspective](https://mlsanity.com/paper/2411.04136/) (2024)
- [LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networks](https://mlsanity.com/paper/2504.14556/) (2025)
- [On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection](https://mlsanity.com/paper/1906.07064/) (2019)
- [From Prompts to Protection: Large Language Model-Enabled In-Context Learning for Smart Public Safety UAV](https://mlsanity.com/paper/2506.02649/) (2025)
- [Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning](https://mlsanity.com/paper/2409.00124/) (2024)
- [Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-context Learning](https://mlsanity.com/paper/2408.02549/) (2024)
- [Deep Reinforcement Learning for Joint Cruise Control and Intelligent Data Acquisition in UAVs-Assisted Sensor Networks](https://mlsanity.com/paper/2312.09953/) (2023)
- [Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making](https://mlsanity.com/paper/2502.05220/) (2025)
- [UAV-assisted Distributed Learning for Environmental Monitoring in Rural Environments](https://mlsanity.com/paper/2407.02693/) (2024)
- [Prompting Wireless Networks: Reinforced In-Context Learning for Power Control](https://mlsanity.com/paper/2506.06526/) (2025)

## Built on by (most cited first, 4 of 4)

- [Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial](https://mlsanity.com/paper/2602.19534/) (2026)
- [AIC-VDS: Attention-Based In-Context Learning for Joint Velocity Control and Data Collection Scheduling in Multi-UAV-Assisted Pipeline Monitoring](https://mlsanity.com/paper/2510.05698/) (2025)
- [Advancing Autonomous Emergency Response Systems: A Generative AI Perspective](https://mlsanity.com/paper/2511.09044/) (2025)
- [LAPF: LLM-Agent-Based Path Finder Using the UAVScenes Dataset](https://mlsanity.com/paper/2608.15175/) (2026)

## Abstract (arXiv)

Uncrewed Aerial Vehicles (UAVs) play a vital role in public safety, especially in monitoring wildfires, where early detection reduces environmental impact. In UAV-Assisted Wildfire Monitoring (UAWM) systems, jointly optimizing the data collection schedule and UAV velocity is essential to minimize the average Age of Information (AoI) for sensory data. Deep Reinforcement Learning (DRL) has been used for this optimization, but its limitations-including low sampling efficiency, discrepancies between simulation and real-world conditions, and complex training make it unsuitable for time-critical applications such as wildfire monitoring. Recent advances in Large Language Models (LLMs) provide a promising alternative. With strong reasoning and generalization capabilities, LLMs can adapt to new tasks through In-Context Learning (ICL), which enables task adaptation using natural language prompts and example-based guidance without retraining. This paper proposes a novel online Flight Resource Allocation scheme based on LLM-Enabled In-Context Learning (FRSICL) to jointly optimize the data collection schedule and UAV velocity along the trajectory in real time, thereby asymptotically minimizing the average AoI across all ground sensors. Unlike DRL, FRSICL generates data collection schedules and velocities using natural language task descriptions and feedback from the environment, enabling dynamic decision-making without extensive retraining. Simulation results confirm the effectiveness of FRSICL compared to state-of-the-art baselines, namely Proximal Policy Optimization, Block Coordinate Descent, and Nearest Neighbor.