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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.16163 |
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| _version_ | 1866917280872398848 |
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| author | Hossen, Md Sharif Dickerson, Cole Ozdemir, Ozgur Gurses, Anil Sarbudeen, Mohamed Rabeek Zajkowski, Thomas Alam, Ahmed Manavi Tucker, Everett Bjorndahl, William Solis, Fred Javed, Sadaf Kamath, Anirudh Tang, Xiangyao Sadique, Joarder Jafor Hermstein, Kevin Liu Mahmud, Kaies Al Viloria, Jose Angel Sanchez Hawkins, Skyler Cui, Yuqing Dey, Annoy Liu, Yuchen Gurbuz, Ali Camp, Joseph Ahmad, Rizwan van der Merwe, Jacobus Mohamed, Ahmed Ibrahim Zussman, Gil Kurum, Mehmet Kamesh, Namuduri Guan, Zhangyu Pados, Dimitris Sklivanitis, George Guvenc, Ismail Sichitiu, Mihail Mushi, Magreth Dutta, Rudra |
| author_facet | Hossen, Md Sharif Dickerson, Cole Ozdemir, Ozgur Gurses, Anil Sarbudeen, Mohamed Rabeek Zajkowski, Thomas Alam, Ahmed Manavi Tucker, Everett Bjorndahl, William Solis, Fred Javed, Sadaf Kamath, Anirudh Tang, Xiangyao Sadique, Joarder Jafor Hermstein, Kevin Liu Mahmud, Kaies Al Viloria, Jose Angel Sanchez Hawkins, Skyler Cui, Yuqing Dey, Annoy Liu, Yuchen Gurbuz, Ali Camp, Joseph Ahmad, Rizwan van der Merwe, Jacobus Mohamed, Ahmed Ibrahim Zussman, Gil Kurum, Mehmet Kamesh, Namuduri Guan, Zhangyu Pados, Dimitris Sklivanitis, George Guvenc, Ismail Sichitiu, Mihail Mushi, Magreth Dutta, Rudra |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16163 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Collection: UAV-Based Wireless Multi-modal Measurements from AERPAW Autonomous Data Mule (AADM) Challenge in Digital Twin and Real-World Environments Hossen, Md Sharif Dickerson, Cole Ozdemir, Ozgur Gurses, Anil Sarbudeen, Mohamed Rabeek Zajkowski, Thomas Alam, Ahmed Manavi Tucker, Everett Bjorndahl, William Solis, Fred Javed, Sadaf Kamath, Anirudh Tang, Xiangyao Sadique, Joarder Jafor Hermstein, Kevin Liu Mahmud, Kaies Al Viloria, Jose Angel Sanchez Hawkins, Skyler Cui, Yuqing Dey, Annoy Liu, Yuchen Gurbuz, Ali Camp, Joseph Ahmad, Rizwan van der Merwe, Jacobus Mohamed, Ahmed Ibrahim Zussman, Gil Kurum, Mehmet Kamesh, Namuduri Guan, Zhangyu Pados, Dimitris Sklivanitis, George Guvenc, Ismail Sichitiu, Mihail Mushi, Magreth Dutta, Rudra Networking and Internet Architecture 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. |
| title | Collection: UAV-Based Wireless Multi-modal Measurements from AERPAW Autonomous Data Mule (AADM) Challenge in Digital Twin and Real-World Environments |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2602.16163 |