T10: Unleashing the Power of Airborne Computing in UAV Systems

Co-organizer: Kejie Lu, University of Puerto Rico at Mayagüez, Puerto Rico
Co-organizer: Yan Wan, University of Texas at Arlington, USA
Co-organizer: Shengli Fu, University of North Texas, USA
Co-organizer: Junfei Xie, San Diego State University, USA

Abstract: In the ever-evolving realm of technology, Unmanned Aerial Vehicles (UAVs) stand out as a beacon of innovation, captivating industries, federal entities, and the academic community. Our endeavors in this domain have been significantly supported by the National Science Foundation (NSF). Initially backed by a major NSF project spanning from 2017 to 2022, our research has now entered its second phase, with a new award commencing this year. As we delve into the multifaceted functionalities of UAVs—including control, communications, networking, and computing—a unified approach to fully harness airborne computing remains a challenge. This tutorial is poised to bridge this divide, heralding a new age of UAV-centric airborne computing.

In this tutorial, we will: (1) illuminate the present and imminent UAV applications, delving into their complexities, (2) highlight real-world case studies, demonstrating the transformative power of airborne computing in reshaping UAV functionalities, (3) reveal essential design strategies, meticulously crafted for the upcoming generation of UAV systems enriched with airborne computing capabilities, (4) present our cutting-edge UAV-based airborne computing platform, along with our most recent prototype, and (5) explore pioneering UAV functions, encompassing reinforcement-learning guided antenna positioning, coding-driven distributed computing and federated learning, software-defined radio-powered cellular base stations, and deep learning- enhanced object detection.

As we draw to a close, we will pave the way for an interactive discussion on the lingering challenges and the expansive future prospects in UAV-based airborne computing. Embark on this enlightening odyssey with us, as we chart the course for the next frontier in UAV systems.

 

Co-organizer’s Bios:

Kejie Lu

Dr. Kejie Lu is a professor in the Department of Computer Science and Engineering, at the University of Puerto Rico at Mayagüez (UPRM). He received his Ph.D. degree in Electrical Engineering from the University of Texas at Dallas in 2003. Since July 2005, he has been a faculty member at UPRM. His research interests include architecture and protocol design for computer and communication networks, cyber-physical systems, network-based computing, and network testbed development.

Yan Wan

Dr. Yan Wan is currently a Distinguished University Professor in the Electrical Engineering Department at the University of Texas at Arlington. She received her Ph.D. degree in Electrical Engineering from Washington State University in 2009. Her research interests lie in developing fundamental theories and tools for the modeling, evaluation, and control tasks in large scale dynamic networks and cyber-physical systems, and their applications to urban aerial mobility, autonomous driving, robot networking, and air traffic management.

Shengli Fu

Dr. Shengli Fu is currently a professor and the Chair in the Department of Electrical Engineering, University of North Texas (UNT), Denton, TX. He received his Ph.D. degree in Electrical Engineering from the University of Delaware, Newark, DE, in 2005, before he joined UNT. His research interests include coding and information theory, wireless communications and sensor networks, aerial networks, and drone systems design.

Junfei Xie

Dr. Junfei Xie is an Associate Professor in the Electrical and Computer Engineering Department at the San Diego State University. She received her Ph.D. degree in Computer Science and Engineering in 2016 from the University of North Texas. Her current research interests include distributed computing, airborne networks, unmanned systems, spatiotemporal data mining, dynamical system modeling and control, and complex information systems.