9th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications
Robot vehicle platforms, often called “drones”, offer exciting new opportunities for mobile computing. Autonomous cooperative systems, made of intelligent devices (such as drones), may deploy and optimize the network to improve its coverage, build routes and fix network partitions to ensure the best communication performance, reduce energy consumption, and dynamically respond to detected network problems. Innovative solutions are built upon these drone networking primitives to accomplish cost-effective and wide-ranging mission-critical applications, including search and rescue, surveillance, 3D-mapping, farmland and construction monitoring, delivery of light-weight objects and products, and video.
DroNet welcomes contributions dealing with all facets of drones as mobile computing platforms, including system aspects, theoretical studies, algorithm and protocol design, as well as requirements, constraints, dependability, and regulations. We are particularly looking for papers reporting on experimental results of deployed systems, summaries of challenges or advancements, measurements, and innovative applications. We welcome in particular also contributions from interdisciplinary teams to present robotic work or applications focusing on the communication networks enabling the efficient control and context-awareness of teams of unmanned autonomous vehicles/systems with an emphasis on civilian and aerial applications, while related work on underwater/space/ground unmanned systems are also invited.
Original submissions of research papers on a diverse range of topics are sought, particularly those identifying new research directions. The topics of the interest for the conference include, but are not limited to:
We invite original research papers that have not been previously published and are not currently under review for publication. All submissions must be provided in PDF format, and follow the formatting guidelines of MobiSys 2023. Full papers must be no longer than 6 pages, and position papers are limited to 4 pages including references. DroNet follows a single-blinded review process.
All submissions must use a 10pt font (or larger) and be correctly formatted for printing on letter-sized (8.5" by 11") paper. Paper text blocks must follow ACM guidelines: double-column, with each column 9.25" by 3.33", 0.33" space between columns, and single-spaced. The abstract should contain less than 250 words.
Submissions can use this LaTex template that is known to comply with the formatting requirements. Authors remain responsible for checking that their resulting PDF meets our formatting and anonymity specifications.
The DroNet workshop will take place Sunday, June 18th. Each presenter should present their research work in 20 minute slot.
Zoom link for following the workshop
| 9:10-9:25 Welcome & Logistics |
| 09:25-10:40 Session #1 |
| Article #1. Towards Robust Lidar-based 3D Detection and Tracking of UAVs by Tasnim Azad Abir (The University of Texas at Arlington); Endrowednes Kuantama, Richard Han, Judith Dawes, Rich Mildren (Macquarie University); Phuc Nguyen (The University of Texas at Arlington) |
| Article #2. Exploring Batteryless UAVs by Mimicking Bird Flight by Rishabh Goel (Georgia Institute of Technology); Tien An Pham, Phuc Nguyen (University of Texas at Arlington); Josiah Hester (Georgia Institute of Technology) |
| Article #3 Analytical Description of Access Probability and RRA strategy for UAV-Aided Vehicular Applications by Francesca Conserva, Roberto Verdone (University of Bologna) |
| Article #4 How to Learn on the Fly? On Improving the Uplink Throughput Performance of UAV-Assisted Sensor Networks by Naresh Babu Kakarla, V Mahendran (Indian Institute of Technology, Tirupati) |
| 10:40-11:00 Coffee Break |
| 11:00-12:35 Session #2 |
| Article #5 Interference by Drones to 5G Ground Users: A Simulation Study byEnrique Caballero (University of Klagenfurt); Aymen Fakhreddine (University of Klagenfurt & Technology Innovation Institute); Christian Bettstetter (University of Klagenfurt). |
| Article #6 Object Recognition Offloading in Augmented Reality Assisted UAV-UGV Systems by Chenyang Wang, Benjamin Carlson, Qi Han (Colorado School of Mines). |
| Article #7 DroneChase: A Mobile and Automated Cross-Modality System for Continuous Drone Tracking by Neel Vora (University of Texas at Arlington); Yi Wu, Jian Liu (University of Tennessee, Knoxville); Phuc Nguyen (University of Texas at Arlington) |
| Article #8 Framework for Realistic Drone and Networking Simulators by Peter Hall, Jonathan Diller, Ava Moon, Qi Han (Colorado School of Mines). |
| 12:35-13:35 Lunch Break |
| 13:35-14:45 Session #3 (Keynote) |
| Prof. Fatemeh Afghah (Director of the Intelligent Systems and Wireless Networking (IS-WiN) Laboratory, Clemson University, USA) Title: Firefighter Drones- Low-altitude Fleet of Drones in Forest Fire Management Abstract: Commercial and civilian applications of drones are becoming increasingly popular, with current applications in aerial photography, shipping and delivery, precision agriculture, remote sensing, and many more yet to come. UAVs have been increasingly utilized in several search and rescue and disaster monitoring operations to collect data/imagery. The current UAV-based operations often involve a single or multiple remotely-controlled UAVs led by commanders in a ground station or pilots in manned aircraft that can still endanger the life of first responders. The full potential capabilities of UAV technology cannot be utilized unless the drones are used in an autonomous mode. Therefore, the future of drone technology is expected to be dominated by smart, small and low-cost autonomous drones that cooperate and coordinate to perform compound missions such as wildfire management with no or minimal human interventions. In this talk, we discuss some of our recent work related to task allocation, communication, and onboard wildfire detection in a network of autonomous UAVs as well as the datasets of aerial images collected by our team during prescribed fires. Bio.: Fatemeh Afghah is an Associate Professor with the Electrical and Computer Engineering Department at Clemson University and the director of the Intelligent Systems and Wireless Networking (IS-WiN) Laboratory. Prior to joining Clemson University, she was an Associate Professor with the School of Informatics, Computing and Cyber Systems, at Northern Arizona University. Her research interests include wireless communication networks, decision-making in multi-agent systems, UAV networks, security, and artificial intelligence in healthcare. Her recent project involves autonomous decision-making in uncertain environments, using autonomous vehicles for disaster management and IoT security. She is the recipient of several awards including the Air Force Office of Scientific Research Young Investigator Award in 2019, the NSF CAREER Award in 2020, NAU's Most Promising New Scholar Award in 2020, Best paper award at INFOCOM WISRAN in 2023, and the NSF CISE Research Initiation Initiative (CRII) Award in 2017. She is the author/co-author of over 130 peer-reviewed publications and served as the associate editor for several journals including Elsevier Journal of Network and Computer Applications, Ad hoc networks, Computer Networks, ACM Transactions on Computing for Healthcare (HEALTH), Springer Neural Processing Letters and the organizer and TPC chair for several international IEEE workshops in the field of UAV communications and AI, including IEEE INFOCOM Workshop on Wireless Sensor, Robot, and UAV Networks (WiSRAN’19), IEEE WOWMOM Workshop on Wireless Networking, Planning, and Computing for UAV Swarms (SwarmNet’20&21&22), 2021 NSF Smart Health PI workshop on “Smart Health in the AI and COVID Era”, 2022 NSF CPS Workshop on “AI in Healthcare”. |