Guest Editors
Assoc. Prof. Vu Khanh Quy
Email: quyvk@utehy.edu.vn
Affiliation: Faculty of Information Technology, Hung Yen University of Technology and Education, Hungyen, Vietnam
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Research Interests: beyond 5G/6G communications, cloud/fog/edge computing, UAV and internet of vehicles, advanced AI techniques, and federated learning
Prof. Abdellah Chehri
Email: chehri@rmc.ca
Affiliation: Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, Canada
Homepage:
Research Interests: internet of things, beyond 5G/6G, big data, data analytics/AI/ML, ML/federated learning in wireless systems, unmanned aerial vehicle communications, cloud/fog/edge computing and networking, smart cities and public safety technology
Summary
The rapid evolution of Industry 4.0 and Beyond 5G/6G networks has accelerated the deployment of Industrial Internet of Things (IIoT) systems in smart manufacturing, logistics, energy, and critical infrastructure. However, conventional terrestrial IIoT architectures face limitations in coverage, scalability, latency, and resilience in dynamic industrial environments. Unmanned Aerial Vehicles (UAVs), with their mobility, flexible deployment, and aerial communication capabilities, provide a promising solution to enhance connectivity, edge intelligence, and data acquisition in industrial ecosystems.
This Special Issue focuses on AI-driven UAV-assisted IIoT systems, emphasizing intelligent architectures, multi-agent learning, federated AI, edge/fog computing integration, resource optimization, and energy-efficient networking. The issue aims to explore advanced AI techniques for autonomous UAV coordination, dynamic task offloading, adaptive routing, spectrum management, and secure data sharing in industrial scenarios. Sustainable and green communication strategies for UAV-enabled IIoT are also encouraged.
We invite original research and review articles that address theoretical models, system design, simulation frameworks, experimental validation, and real-world industrial deployments, advancing the state of the art in AI-driven UAV-assisted IIoT systems.
Topics of Interest (but not limited to):
- AI-driven UAV-enabled IIoT architectures
- Multi-agent reinforcement learning for UAV swarms
- Federated learning in UAV-assisted industrial systems
- UAV-based data collection and task offloading
- Resource allocation and trajectory optimization
- Energy-efficient and green UAV-IIoT networking
- Secure and privacy-preserving industrial UAV systems
- Blockchain-enabled UAV-IIoT frameworks
Keywords
artificial intelligence (AI), IIoT, UAV, federated learning, deep reinforcement learning, edge intelligence, 5G/6G networks, energy-efficient communications, smart manufacturing, digital twin, industrial automation, resource optimization.
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