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

• URL: http://arxiv.org/abs/2507.10134v1
• Date: Mon, 14 Jul 2025 10:24:43 GMT
• Title: FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoring
• Authors: Yousef Emami, Hao Zhou, Miguel Gutierrez Gaitan, Kai Li, Luis Almeida,
• Abstract summary: Unmanned Aerial Vehicles (UAVs) are vital for public safety, particularly in wildfire monitoring, where early detection minimizes environmental impact.<n>In UAV-Assisted Wildfire Monitoring (UAWM) systems, joint optimization of sensor transmission scheduling and velocity is critical for minimizing Age of Information (AoI) from stale sensor data.<n>Deep Reinforcement Learning (DRL) has been used for such optimization; however, its limitations such as low sampling efficiency, simulation-to-reality gaps, and complex training render it unsuitable for time-critical applications like wildfire monitoring.<n>This paper introduces a new online Flight Resource Allocation scheme based on
• Score: 14.068881151569435
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Unmanned Aerial Vehicles (UAVs) are vital for public safety, particularly in wildfire monitoring, where early detection minimizes environmental impact. In UAV-Assisted Wildfire Monitoring (UAWM) systems, joint optimization of sensor transmission scheduling and velocity is critical for minimizing Age of Information (AoI) from stale sensor data. Deep Reinforcement Learning (DRL) has been used for such optimization; however, its limitations such as low sampling efficiency, simulation-to-reality gaps, and complex training render it unsuitable for time-critical applications like wildfire monitoring. This paper introduces a new online Flight Resource Allocation scheme based on LLM-Enabled In-Context Learning (FRSICL) to jointly optimize the UAV's flight control and data collection schedule along the trajectory in real time, thereby asymptotically minimizing the average AoI across ground sensors. In contrast to DRL, FRSICL generates data collection schedules and controls velocity using natural language task descriptions and feedback from the environment, enabling dynamic decision-making without extensive retraining. Simulation results confirm the effectiveness of the proposed FRSICL compared to Proximal Policy Optimization (PPO) and Nearest-Neighbor baselines.

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