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

Technical Deep Dive: FRSICL for LLM-Enabled UAV-Assisted Wildfire Monitoring

Overview

The paper proposes a novel "Flight Resource Allocation scheme using LLM-Enabled In-Context Learning (FRSICL)" for UAV-Assisted Wildfire Monitoring (UAWM) systems. FRSICL aims to minimize the average Age of Information (AoI) across a network of ground sensors by jointly optimizing the UAV's data collection schedule and velocity.

Problem & Context

• Uncrewed Aerial Vehicles (UAVs) play a vital role in wildfire monitoring by collecting real-time sensor data from distributed ground stations.
• Timely data collection is critical, as outdated information can lead to inaccurate situational awareness and put responders at risk.
• The Age of Information (AoI) metric quantifies data freshness by measuring the time difference between the current time and the generation timestamp of the last sensor measurement received.
• Jointly optimizing the UAV's data collection schedule and velocity is challenging, as aggressive maneuvers can degrade link quality, while overly conservative movements extend mission time and delay data acquisition.

Methodology

• FRSICL leverages Large Language Models (LLMs) and In-Context Learning (ICL) to address the joint optimization problem.
• The system uses an edge-hosted LLM to:
  1. Analyze the current state (AoI, channel conditions, UAV location)
  2. Generate optimized data collection schedules and UAV velocities
  3. Iteratively improve decisions based on performance feedback
• Safety constraints are explicitly embedded into the scheduling logic, including:
  • Velocity-constrained decision making
  • Channel-aware sensor selection
  • Diversity-preserving scheduling rules

Data & Experimental Setup

• Simulations consider a scenario with 10 randomly placed ground sensors in a 100m x 100m area.
• The proposed FRSICL is compared against baselines such as Proximal Policy Optimization (PPO), Block Coordinate Descent (BCD), Nearest Neighbor, Genetic Algorithm, and Deep Q-Network.
• Key parameters include maximum UAV velocity (15 m/s), maximum transmit power (100 mW), and 30 time steps per episode.

Results

• FRSICL significantly outperforms the baselines, achieving faster convergence and lower average AoI (around 5-6 seconds).
• FRSICL with the more capable o3-mini LLM outperforms the GPT-4O-mini version.
• As the number of sensors increases from 5 to 15, FRSICL maintains a lower AoI than the Nearest Neighbor baseline.
• Compared to PPO, FRSICL exhibits more stable and efficient UAV velocity profiles, avoiding abrupt changes.

Interpretation

• FRSICL's advantages over conventional DRL methods stem from its training-free adaptation, iterative policy refinement, and transparent decision-making.
• The structured, prompt-based approach of FRSICL improves reproducibility and interpretability compared to opaque, hyperparameter-dependent DRL.
• The performance gains of FRSICL highlight the potential of LLM-enabled ICL for enhancing the responsiveness and reliability of UAV systems in time-critical applications like wildfire monitoring.

Limitations & Uncertainties

• The study is limited to a single UAV scenario, while real-world UAWM may involve multiple coordinated UAVs.
• The proposed model assumes a fixed UAV trajectory, whereas dynamic path planning could further improve performance.
• The impact of LLM vulnerabilities, such as prompt injection attacks, on the reliability of FRSICL requires further investigation.

What Comes Next

• Future research will explore extending FRSICL to multi-UAV coordination strategies, incorporating dynamic path planning, and addressing LLM security challenges.
• Experimental validation on real-world UAWM deployments would provide valuable insights into the practical applicability and performance of the proposed framework.