# AIC-VDS: Attention-Based In-Context Learning for Joint Velocity Control and Data Collection Scheduling in Multi-UAV-Assisted Pipeline Monitoring

arXiv: https://arxiv.org/abs/2510.05698 · HTML: https://mlsanity.com/paper/2510.05698/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities](https://mlsanity.com/paper/2309.16739/) (2023)
- [The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis](https://mlsanity.com/paper/2311.00237/) (2023)
- [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)
- [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)
- [FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoring](https://mlsanity.com/paper/2507.10134/) (2025)

## Built on by (most cited first, 1 of 1)

- [Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial](https://mlsanity.com/paper/2602.19534/) (2026)

## Abstract (arXiv)

Uncrewed aerial vehicles (UAVs) are increasingly deployed for autonomous inspection and sensor data collection in large-scale infrastructure monitoring applications, such as pipeline monitoring, where timely anomaly detection is critical. Jointly optimizing data-collection schedules and flight velocities is a critical challenge, as inefficiencies can increase packet loss and inspection latency. While online deep reinforcement learning (DRL) is a widely investigated approach, it suffers from low sample efficiency, substantial training requirements, and simulation-to-reality gaps in time-sensitive scenarios. Large language models (LLMs) offer a promising alternative through in-context learning (ICL); however, their substantial input requirements can introduce considerable computational and communication overhead. To address this, we propose Attention-Based In-Context Learning for Velocity Control and Data Collection Scheduling (AIC-VDS), a joint optimization framework designed to minimize packet loss under partial and potentially outdated local network-state information. AIC-VDS utilizes an attention module to process real-time network-state data, including sensor battery levels, sensor queue lengths, communication channel conditions, UAV locations, time since the previous sensor visit, and sensor urgency scores. This module extracts task-relevant features to reduce input overhead before querying the LLM. The LLM leverages these compressed natural-language prompts to generate adaptive data-collection schedules and velocity-control decisions for UAV execution. Simulation results show that the attention-based representation reduces the average prompt length by 50%, while AIC-VDS rapidly stabilizes packet loss in the considered scenario.