Collection: UAV-Based Wireless Multi-modal Measurements from AERPAW Autonomous Data Mule (AADM) Challenge in Digital Twin and Real-World Environments
Md Sharif Hossen, Cole Dickerson, Ozgur Ozdemir, Anil Gurses, Mohamed Rabeek Sarbudeen, Thomas Zajkowski, Ahmed Manavi Alam, Everett Tucker, William Bjorndahl, Fred Solis, Sadaf Javed, Anirudh Kamath, Xiangyao Tang, Joarder Jafor Sadique, Kevin Liu Hermstein, Kaies Al Mahmud, Jose Angel Sanchez Viloria, Skyler Hawkins, Yuqing Cui, Annoy Dey, Yuchen Liu, Ali Gurbuz, Joseph Camp, Rizwan Ahmad, Jacobus van der Merwe, Ahmed Ibrahim Mohamed, Gil Zussman, Mehmet Kurum, Namuduri Kamesh, Zhangyu Guan, Dimitris Pados, George Sklivanitis, Ismail Guvenc, Mihail Sichitiu, Magreth Mushi, Rudra Dutta
TL;DR
An unmanned aerial vehicle (UAV) wireless dataset collected as part of the AERPAW Autonomous Aerial Data Mule (AADM) challenge, organized by the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) project, which supports reproducible research on autonomous UAV networking, multi-cell association and scheduling, air-to-ground propagation modeling, DT-to-real-world transfer learning, and integrated sensing and communication.
Abstract
In this work, we present an unmanned aerial vehicle (UAV) wireless dataset collected as part of the AERPAW Autonomous Aerial Data Mule (AADM) challenge, organized by the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) project. The AADM challenge was the second competition in which an autonomous UAV acted as a data mule, where the UAV downloaded data from multiple base stations (BSs) in a dynamic wireless environment. Participating teams designed flight control and decision-making algorithms for choosing which BSs to communicate with and how to plan flight trajectories to maximize data download within a mission completion time. The competition was conducted in two stages: Stage 1 involved development and experimentation using a digital twin (DT) environment, and in Stage 2, the final test run was conducted on the outdoor testbed. The total score for each team was compiled from both stages. The resulting dataset includes link quality and data download measurements, both in DT and physical environments. Along with the USRP measurements used in the contest, the dataset also includes UAV telemetry, Keysight RF sensors position estimates, link quality measurements from LoRa receivers, and Fortem radar measurements. It supports reproducible research on autonomous UAV networking, multi-cell association and scheduling, air-to-ground propagation modeling, DT-to-real-world transfer learning, and integrated sensing and communication, which serves as a benchmark for future autonomous wireless experimentation.