UAV Trajectory and Bandwidth Allocation for Efficient Data Collection in Low-Altitude Intelligent IoT: A Hierarchical DRL Approach

2026-04-25

AI Summary

This work addresses the challenges of unknown interference, dynamic data volumes, and limited onboard computational resources in unmanned aerial vehicle (UAV)-based data collection within low-altitude intelligent Internet of Things (IoT) systems. To tackle these issues, the authors propose a lightweight hierarchical deep reinforcement learning framework, termed TBH-DDPG, which jointly optimizes data collection efficiency through coarse-grained trajectory planning at the upper level and fine-grained bandwidth allocation at the lower level. Evaluated in realistic environments featuring interference sources, time-varying data demands, and diverse obstacles, the proposed method achieves rapid convergence and low computational overhead. Compared to non-hierarchical approaches, it improves convergence speed by 44.44% and reduces computational cost by 58.05%, significantly enhancing total data throughput and overall system performance.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Under the 6G wireless network evolution, the low-altitude Internet of Things (IoT), supported by unmanned aerial vehicles (UAVs) with Integrated Sensing and Communication (ISAC) capabilities, provides ground sensing networks with advanced real-time monitoring and data collection. To maximize data collection volume from distributed IoT nodes, AI-powered data collection technology plays a critical role in enabling intelligent decision-making. Among them, deep reinforcement learning (DRL) has gained particular attention. However, the existing DRL-based work on UAV-assisted IoT nodes data collection rarely address problems such as unknown interference and dynamic data volume. Moreover, these DRL models have high arithmetic requirements and slow convergence speed, making it difficult to carry on UAVs with limited load and arithmetic power. To address these challenges, a hierarchical deep reinforcement learning (HDRL), which can converge quickly and with smaller models, is designed to optimize UAV trajectories and bandwidth allocation to maximize data collection volume. Firstly, the proposed scenario incorporates interference from jammers, dynamic data volume of IoT nodes, and multiple types of obstacles. The entire task is hierarchically structured: the upper-level makes flight trajectory decisions at a coarse temporal granularity, while the lower-level makes bandwidth allocation decisions at a finer temporal granularity. Secondly, a trajectory and bandwidth allocation optimization algorithm based on hierarchical deep deterministic policy gradients (TBH-DDPG) is proposed to solve the problem. Finally, simulation results demonstrate that the proposed algorithm improves convergence speed by 44.44%, and reduces computational cost by 58.05%, compared to non-hierarchical algorithm.

Problem

• UAV trajectory
• bandwidth allocation
• low-altitude IoT
• dynamic data volume
• unknown interference

Innovation

• Hierarchical Deep Reinforcement Learning
• UAV Trajectory Optimization
• Bandwidth Allocation
• ISAC-enabled IoT
• Low-altitude Intelligent IoT