Wi-DroIT 2026

Reykjavik, Iceland

June 22-24, 2026

Co-located with DCOSS 2026

Submission Link

Scope

Recently, the rapid development of Unmanned Autonomous Vehicles (UAVs), also known as drones, has highlighted a plethora of emergent applications such as infrastructure inspection and surveillance, smart agriculture, seek and rescue, parcel delivery, communications, and post-disaster recovery. In all these applications UAVs may coexist with ground Internet of Things (IoT) devices. For example, UAVs autonomously perform remote sensing relaying the collected data by the sensors. Additionally, one or more UAVs can collaborate to provide services that require efficient protocols where multiple objectives and constraints need to be considered.

For this workshop, we seek papers that combine the design of algorithms and testbed implementations to develop the theoretical foundations for UAVs as well as the synergies with IoT devices. The numerous emergent applications raised by IoT may require an interdisciplinary approach involving techniques from algorithm foundations and different areas, such as computer networks, artificial intelligence, concurrent, parallel, and distributed computing, security, digital signal, image, and sound processing.

Author Information

Authors are invited to submit original unpublished manuscripts that demonstrate current research on distributed sensor systems related to Wi-DroIT topics of interest. Please use the US letter size (8.5 x 11 in) standard IEEE conference LaTeX format or Microsoft Word template available here.

All submissions should be written in English and submitted as a full paper. Full papers should have a maximum of six (6) printed pages including figures and references. Full papers are expected to describe fully developed ideas with a thorough evaluation. Note that Wi-DroIT does not follow the double-blind review policy. The names and affiliations of all the authors must be present in the submitted manuscript.

Topics of Interest

• Autonomous sensing via UAVs
• Topology monitoring with UAVs
• Remote sensing networks via UAVs
• Communication protocols of UAVs over IoT
• Modeling and analysis of UAVs over IoT
• Precision agriculture and UAVs
• High-altitude platform station

• Crops monitoring in agriculture
• Bugs monitoring in agriculture
• UAVs for environmental monitoring
• Autopilot and UAS autonomy
• UAVs path planning and scheduling
• Parcel delivery using UAVs
• Hierarchical aerial computing: collaboration of UAVs and HAP

• Cellular networks and UAVs
• Constrained and multi-objectives problems
• Sensors localization with UAVs
• UAVs tracking techniques
• Cooperative control of multiple UAVs
• Cyber-security communications and UAVs
• Flocking of multi-UAV

• Optimal UAV deployment strategies
• Test-beds and experimental results for UAVs
• Single UAV applications
• Multi-UAV applications
• Energy-efficient UAV communications
• Machine learning for UAV communications
• Multi-UAV cooperative edge computing

Committees

Workshop Chairs

Lorenzo Palazzetti - Pennsylvania State University, PA, USA
Dimitrios Zorbas - Nazarbayev University, Kazakhstan

Steering Committee

Enrico Natalizio - Technology Innovation Institute, United Arab Emirates
Cristina M. Pinotti - University of Perugia, Italy
Federico Corò - University of Padova, Italy
Francesco Betti Sorbelli - University of Perugia, Italy

Program Committee

Alfredo Navarra - Università degli Studi di Perugia
Nathaniel Hudson - Illinois Institute of Technology, IL, USA
Anurag Satpathy - Missouri S&T, MO, USA
Amin Kargar - Tyndall National Institute, Ireland
Alessandro Brighente - University of Padova

Xu Tao, Kennesaw State University, GA, USA
Arindam Khanda - Missouri S&T, MO, USA
Pietro Tedeschi - Leonardo S.p.A., Italy
Punyasha Chatterjee - Jadavpur University, India
Savio Sciancalepore - Eindhoven University of Technology (TU/e), Holland
Tathagata Mukherjee - University of Alabama in Huntsville, AL, USA
Ziheng Fu - Pennsylvania State University, PA, USA
Giorgia Pazzi - University of Pisa, Italy

Publicity Chair

Giorgia Pazzi - University of Pisa, Italy

Web Chair

Lorenzo Palazzetti - Pennsylvania State University, PA, USA

Program

June 22-24, 2026 — Reykjavik, Iceland

Morning Session — Chair: Lorenzo Palazzetti
10.30–10.50	Wi-DroIT 2026 Workshop Opening
10.50–11.50 — Session 1: “AI & Machine Learning for Livestock and Agriculture”
	From Drones to Labels: A Semi-Automated Pipeline for Efficient Livestock Segmentation Annotation Lorenzo Palazzetti, Kuan-Ling Chen, Enrico Casella
	Crop and Weed Segmentation by an Ensemble of Distilled Models Andrei Vicol, Dan Popescu, Loretta Ichim
	Blockchain-based UAV Marketplace with Machine Learning for Livestock Health Prediction Khouloud Hwerbi, Ichrak Amdouni, Cedric Adjih, Anis Laouiti, Leila Azouz Saidane
11.50–12.00	Break
12.00–13.00 — Session 2: “UAV Systems, Autonomy & Infrastructure”
	Fault Tolerance for MAVLink-based Mission Programs Using Checkpointing and Logging Aggelos Tzikas, Manos Koutsoubelias, Spyros Lalis
	Automatic Landing and Battery Charging of a Multicopter Using an Intelligent Solution Marius Adrian Dima, Dan Popescu, Andrei Vicol, Loretta Ichim
	Data Fusion Platform for UAV-based Situational Awareness Using LiDAR Processing and 3D Reconstruction Rajitha Manellanga, Xenios Theodorou, Michalis Demetriou, Konstantinos Manousakis, Georgios Ellinas, Panayiotis Kolios
13.00–14.00	Lunch
Keynote & Closing — Chair: Francesco Betti Sorbelli
14.00–14.45 — Keynote
	From Pixels to Phenotypes: Applied AI and IoT Sensing for Poultry and Livestock Health and Productivity Enrico Casella
14.45–15.00	Workshop Closing

Keynote

Enrico Casella

From Pixels to Phenotypes: Applied AI and IoT Sensing for Poultry and Livestock Health and Productivity

Dr. Enrico Casella is a computer scientist with a highly multi-disciplinary background. His research applies artificial intelligence to complex systems, primarily within the animal science domain. He is the principal investigator of the Cyber-physical Animal Systems Lab (CAS Lab). His team develops data-driven solutions from heterogeneous sensor data. His most recent work focuses on computer vision solutions for animal health, growth and development.