W5: Workshop on Artificial Intelligence for Autonomous Vehicular Mobile Networks

Workshop Organizer: Yu-Jia Chen, National Central University, Taiwan
Workshop Organizer: Zehui Xiong, Singapore University of Technology and Design, Singapore
Workshop Organizer: Chun-Hung Liu, Mississippi State University, USA
Workshop Organizer: Ying Loong Lee, University Tunku Abdul Rahman, Malaysia
Workshop Organizer: Zhijin Qin, Queen Mary University of London, UK

Abstract: With the advancement of sensing, communications and networking, more and more communicating vehicles are being deployed to make our daily life incredibly convenient and enjoyable. For example, unmanned aerial vehicles (UAVs) or Internet of drones (IoD) play a vital role to support various applications including medical, industrial, agricultural, and public safety. Together with the upcoming B5G/6G technologies, it is expected that connected vehicular systems will become more ubiquitous and practical. In particular, artificial intelligence (AI) and machine learning (ML) techniques can provide significant benefits towards automating the tasks of sensing, computing, and communicating in the vehicular mobile networks.

To realize real-time perception and autonomous control, computing and communications in AI-enabled vehicular mobile networks can be more complex and heterogeneous than before. Additionally, due to the characteristics of complex systems (e.g., high mobility of nodes and unreliable link connectivity), vulnerable end devices, limited resources, heterogeneous networking, etc., security and privacy in vehicular mobile networks are extremely challenging in terms of computing, communication, caching, data processing, etc. For example, data collected from sensors for AI-based approaches pose new security threats and ML models trained at edge devices suffer from various malicious attacks. Since communicating vehicles with limited computing power cannot perform complex encryption and decryption of large datasets, traditional security solutions cannot directly be applied.

The objective of this workshop is therefore to bring together state-of-the-art innovations and research activities (both in academia and industry) to explore autonomous vehicular mobile network technologies. This workshop will also provide a venue for exchanging ideas and networking experts/researchers/engineers/students involved in the area of autonomous vehicular mobile networks.

Topics of interest include but are not limited to:

• Edge learning in vehicular mobile networks
• Joint control and communication design for autonomous vehicular mobile networks
• Deep learning and distributed machine learning for vehicular mobile networks
• AI techniques for radio environment awareness
• Reinforcement learning for network decision making, network control, and management
• Reinforcement learning for self-organized vehicular mobile networks
• Privacy-preserving machine learning for autonomous vehicular mobile networks
• Security defense techniques in autonomous vehicular mobile networks
• Energy-efficient architectures/solutions for autonomous vehicular mobile networks
• Blockchain-enabled autonomous vehicular mobile networks
• Fault detection and self-healing in vehicular mobile networks
• Connected and autonomous aerial networks
• New intelligent transportation systems and services in vehicular mobile networks
• Standardizations, prototypes, and performance evaluation of autonomous vehicular mobile networks

Workshop Program

Part 1 – Opening / Welcome

Part 2 – Keynote Speaker – Geoffrey Ye Li, Imperial College London
Title: Deep Learning in Physical Layer Communications

Part 3 – Technical Paper – Peak Age of Information Minimization in UAV-assisted Cognitive Relay Networks

Part 4 – Technical Paper – Towards Cost-efficient Reliable Vehicle-MEC Connectivity for B5G Mobile Networks: Challenges and Future Directions

Part 5 – Closing Remarks