Track Co-Chairs:
Andreas Reinhardt, TU Clausthal, Germany, andreas.reinhardt@tu-clausthal.de
Zhuo Lu, University of Southern Florida, USA, zhuolu@usf.edu
Description:
The Track on AI and Emerging Topics in Networking (AIETN) will bring together researchers who are interested in emerging research areas driven by recent advancements in machine learning, AI, and data analytics technologies, when applied to computing, communications, and networking. This track will provide a venue for discussing innovative ideas and debating future research agendas in the wide range of AI-based and emerging computer networking topics. The submitted papers will be evaluated based on the originality, technical contribution, and the potential for future applications. We also welcome submissions that demonstrate early-stage work and preliminary results on innovative ideas to stimulate more discussions on advancing the networking technologies in various aspects.
- Big data analytics and data-driven designs for future networking protocols
- AI/ML-based designs for computer networks
- Adversarial machine learning and GANs in communications and networking
- Intelligent wireless and spectrum management systems
- Information-Centric Networking
- Multimedia networking
- Future Network Architectures, Internet of Things, and Smart Cities
- Emerging networking techniques for autonomous driving, intelligent transportation systems, robotics, and the tactile Internet
- Unmanned aerial vehicle and unmanned underwater vehicle (UAV/UUV)
- Management and coordination of large-scale distributed networks, e.g., nano-networks, swarms, or the Internet of Things
- Wearable, intra-body and human-centric networking
- Crowdsensing and crowdsourcing
- Emerging designs and paradigms in security and privacy
- Disruptive concepts for the Future Internet
- Quality of Service and Quality of Experience in next generation ad hoc networks
- Machine learning foundation and models for IoT and applications
- Security, Privacy, and Trust for Artificial Intelligence
- Machine learning and distributed machine learning for resource management
- Edge Intelligence: Management of distributed machine learning tasks
- Federated learning management and its applications
- Innovative communication and networking technologies for embedded and/or wearable sensors
- Permissioned Blockchains
- Smart NICs and NIC based virtualization
- Programmable switch based networking innovations
- Emerging ultra-high speed PHY/MAC technologies
- Hardware acceleration of network protocols
- Virtual and Augmented reality for Cyberphysical networks
- Boris Koldehofe, University of Groningen
- Michael Welzl, University of Oslo
- Xenofon Fafoutis, Technical University of Denmark
- Amr Rizk, University of Duisburg-Essen
- Antonio Virdis, University of Pisa
- Damla Turgut, University of Central Florida
- Xiaonan Zhang, Florida State University
- Mamatas Eleftherios, University of Macedonia
- Shangqing Zhao, University of Okalahoma
- Tao Wang, New Mexico State University
- Yalin Sagduyu, Intelligent Automation Inc
- Tao Li, IUPUI, USA
- Moinul Hossain, Towson University
- Tao Hou, University of South Florida
- Ertan Onur, Middle East Technical University
- Hongliang Li, Jilin University
- Jiacheng Shang, Montclair State University
- Zhuozhao Li, Southern University of Science and Technology
- Yunsheng Wang, Kettering university
- Ting Li, Oxford College of Emory University
- Xin Li, Nanjin University of Aeronautics and Astronautics