LAURA: LLM-Assisted UAV Routing for AoI Minimization

2025-03-29

AI Summary

This work addresses the path optimization problem for minimizing the Age of Information (AoI) in unmanned aerial vehicle (UAV)-assisted wireless sensor networks. Method: We propose a hybrid optimization framework integrating large language models (LLMs), evolutionary algorithms, and exact AoI modeling. Crucially, we innovate by embedding an LLM as an intelligent crossover operator within the evolutionary search process, significantly enhancing exploration efficiency in high-dimensional routing solution spaces and improving global convergence. Contribution/Results: Evaluated on multi-node real-time data collection scenarios, our method effectively reduces the maximum AoI, outperforming classical heuristics and state-of-the-art metaheuristics. To the best of our knowledge, this is the first study to incorporate LLMs into AoI-driven UAV trajectory optimization. The proposed framework establishes a scalable new paradigm for timeliness-aware sensing systems in low-altitude economy applications.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

With the rapid growth of the low-altitude economy, there is increasing demand for real-time data collection using UAV-assisted wireless sensor networks. This paper investigates the problem of minimizing the age of information (AoI) in UAV-assisted wireless sensor networks by optimizing the UAV flight routing. We formulate the AoI minimization task and propose a large language model (LLM)-assisted UAV routing algorithm (LAURA). LAURA employs an LLM as intelligent crossover operators within an evolutionary optimization framework to efficiently explore the solution space. Simulation results show that LAURA outperforms benchmark methods in reducing the maximum AoI, especially in scenarios with a large number of sensor nodes.

Problem

• Minimize Age of Information in UAV networks
• Optimize UAV flight routing using LLM
• Improve real-time data collection efficiency

Innovation

• LLM-assisted UAV routing algorithm
• Evolutionary optimization framework
• Intelligent crossover operators