mlsanityData to 14 Oct 2026

LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networks

arXiv 2504.14556 ↗First posted 20 Apr 2025cs.AI cs.ET cs.LGcited by 12

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Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) are increasingly being utilized in various private and commercial applications, e.g., traffic control, parcel delivery, and Search and Rescue (SAR) missions. Machine Learning (ML) methods used in UAV-Assisted Sensor Networks (UASNETs) and, especially, in Deep Reinforcement Learning (DRL) face challenges such as complex and lengthy model training, gaps between simulation and reality, and low sampling efficiency, which conflict with the urgency of emergencies, such as SAR missions. In this paper, an In-Context Learning (ICL)-Data Collection Scheduling (ICLDC) system is proposed as an alternative to DRL in emergencies. The UAV collects sensory data and transmits it to a Large Language Model (LLM), which creates a task description in natural language. From this description, the UAV receives a data collection schedule that must be executed. A verifier ensures safe UAV operations by evaluating the schedules generated by the LLM and overriding unsafe schedules based on predefined rules. The system continuously adapts by incorporating feedback into the task descriptions and using this for future decisions. This method is tested against jailbreaking attacks, where the task description is manipulated to undermine network performance, highlighting the vulnerability of LLMs to such attacks. The proposed ICLDC significantly reduces cumulative packet loss compared to both the DQN and Maximum Channel Gain baselines. ICLDC presents a promising direction for intelligent scheduling and control in UASNETs.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 17 of its 45 references that are in the map. First posted on arXiv 20 Apr 2025.

Builds on

17 of 45 references.

References · 17

  1. 2024Jailbreak Attacks and Defenses Against Large Language Models: A Surveycited by 230
  2. 2023Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attackscited by 194
  3. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 186
  4. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 128
  5. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 90
  6. 2025UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobilitycited by 49
  7. 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
  8. 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Surveycited by 28
  9. 2024Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trendscited by 26
  10. 2024Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Controlcited by 19
  11. 2024Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detectioncited by 16
  12. 2024Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunitiescited by 14
5 more
  1. 2024Large Language Models for Wireless Networks: An Overview from the Prompt Engineering Perspectivecited by 12
  2. 2019On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collectioncited by 11
  3. 2024Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learningcited by 9
  4. 2023Deep Reinforcement Learning for Joint Cruise Control and Intelligent Data Acquisition in UAVs-Assisted Sensor Networkscited by 5
  5. 2025Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Makingcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 6.

12 papers.

  1. 2025UAVBench: An Open Benchmark Dataset for Autonomous and Agentic AI UAV Systems via LLM-Generated Flight Scenarioscited by 13
  2. 2025From Prompts to Protection: Large Language Model-Enabled In-Context Learning for Smart Public Safety UAVcited by 10
  3. 2025FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoringcited by 4
  4. 2026Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorialcited by 4
  5. 2025AIC-VDS: Attention-Based In-Context Learning for Joint Velocity Control and Data Collection Scheduling in Multi-UAV-Assisted Pipeline Monitoringcited by 1
  6. 2025Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awarenesscited by 0
  7. 2025Advancing Autonomous Emergency Response Systems: A Generative AI Perspectivecited by 0
  8. 2026Low-Rank Compression of Pretrained Models via Randomized Subspace Iterationcited by 0
  9. 2026Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivitycited by 0
  10. 2026LLM-Centric Agentic AI for UAV Swarms: Architecture, Enabling Technologies, and Open Problemscited by 0
  11. 2026LAPF: LLM-Agent-Based Path Finder Using the UAVScenes Datasetcited by 0
  12. 2026Security and Privacy in Large-Model-Driven Embodied Agents: Attacks, Defenses, and Future Directionscited by 0

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  1. 2025Large Language Model-Empowered Decision Transformer for UAV-Enabled Data Collection
  2. 2025LLM-Land: Large Language Models for Context-Aware Drone Landing
  3. 2026Context-Aware Information Transfer via Digital Semantic Communication in UAV-Based Networks
  4. 2024Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-based Approach
  5. 2024Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach
  6. 2025Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization
  7. 2026Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning
  8. 2024Goal-Oriented UAV Communication Design and Optimization for Target Tracking: A MachineLearning Approach