Optimization Monitoring Wildfires Sensors Real-time Systems Temperature Sensors Schedules Adaptation Models Data Collection Large Language Models (llms) Network Edge Autonomous Aerial Vehicles Age Of Information (aoi) In-context Learning (icl) Uncrewed Aerial Vehicles (uavs)

External authors:

• Yousef Emami ( Instituto Politecnico do Porto )
• Hao Zhou ( McGill University )
• Kai Li ( Carnegie Mellon University , Instituto Politecnico do Porto )
• Luis Almeida ( Universidade do Porto )

Abstract:

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 article proposes a novel online flight resource allocation scheme based on LLM-enabled ICL (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 (PPO), block coordinate descent (BCD), and nearest neighbor (NN).