Scope and Objectives

Internet of Vehicles (IoV) empower vehicles to communicate with the surrounding environment and remote servers, such as neighbouring cars, roadside infrastructure, and traffic control canters, cloud/edge computing servers, enabling a wide range of on-the-go services, including road safety, infotainment, and intelligent transportation. To better support IoV, various networks (terrestrial networks, aerial networks and satellite networks) and heterogeneous resources (communication, computing and storage) expects to be integrated to provide service to vehicles anywhere and anytime. In such a dynamic and complex scenario, many technical challenges arise, e.g, high mobility of vehicles, stringent of service requirements, multi-dimensional randomness, various dynamics, great heterogeneity, etc. Artificial intelligence (AI) has great potential to address these technical challenges and manage heterogeneous resources efficiently to meet different quality of service (QoS) requirements of IoV. This AIIOV SIG group aims to provide a platform for researchers and developers from both industry and academia to exchange ideas, discuss key technologies, and share latest results, to promote the development of AI empowered IoV.  

Chair

Ning Lu, Queen’s University, Canada

Vice-chairs

Xianfu Chen, VTT Technical Research Centre of Finland, Finland
Alagan Anpalagan, Ryerson University, Canada
Ning Zhang, University of Windsor, Canada
Peng Yang, Huazhong University of Science and Technology, China

Founding Members

Mehdi Bennis, University of Oulu, Finland
Hongbing Liang, Southwest Jiaotong University, China
Siyu Lin, Beijing Jiaotong University, China
Jussi Kangasharju, University of Helsinki, Finland
Yi Zhou, Henan University, China
Tao Huang, James Cook University, Australia
Mohamed Mahmoud, Tennessee Technological University, USA
Chunhe Song, Chinese Academy of Sciences, China
Dajiang Chen, University of Electronic Science and Technology of China, China
Mohammad S Khan, East Tennessee State University, USA
Peng Yang, Huazhong University of Science and Technology, China
Xiaojie Fang, Harbin Institute of Technology, China
Sai Mounika Errapotu, University of Texas at El Paso, USA

Full Proposal

Virtual Seminar Series

• RingSFL: An Adaptive Split Federated Learning Towards Taming Client Heterogeneity – Dr. Nan Cheng, Xidian University, China. September 1, 2023 at 9 AM ET.
  [Abstract and Author Bio] | [Slides] | [Recording]