Replication Dataset for: \A Hierarchical Optimal Control Framework for UAV-Assisted Data Collection in Urban Environments\

This dataset provides the comprehensive simulation scenarios, input configurations, and raw performance metrics required to replicate the experiments presented in the paper: \[Your Paper Title Here]\. The research addresses the complex problem of coordinating a fleet of Unmanned Aerial Vehicles (UAVs) for time-sensitive data collection from Internet of Things (IoT) devices in complex 3D urban environments.The dataset is specifically designed to evaluate our proposed Hierarchical Optimal Control for UAV-assisted Data Collection (HOC-UADC) framework against several baseline algorithms. It includes:1.  **Urban Environment Models**: Detailed 3D city map data for both dense and sparse urban scenarios, including building footprints and heights.2.  **Task Configurations**: A wide range of task sets with varying numbers of IoT devices (N=10 to 80), each defined by its location, data volume, and deadline.3.  **UAV Fleet Parameters**: Initial states and performance parameters for different fleet sizes (M=8, M=10).4.  **Raw Performance Results**: Detailed CSV files containing key performance indicators (KPIs) such as mission completion time, task success rate, energy consumption, and computation time for HOC-UADC (including its ablation variants) and all baseline methods.5.  **Visualization Logs**: Detailed trajectory and scheduling logs for a representative scenario, enabling the visualization of UAV paths and task execution Gantt charts.This dataset is valuable for researchers in autonomous systems, wireless communications, and operations research, providing a standardized benchmark for developing and evaluating multi-UAV coordination and trajectory optimization algorithms.