AiS-IoT 2021

Important Dates

Workshop Organizers

Yubin Zhao, Sun Yat-Sen University, China
zhaoyb23@mail.sysu.edu.cn
Huaming Wu, Tianjin University, China; whming@tju.edu.cn
Xiaofan Li, Jinan University, China; lixiaofan@jnu.edu.cn

Program Committee (tentative)

Bin Liu, University of Strathclyde, UK
Chaogang Tang, China University of Mining and Technology, China
Haneul Ko, Korea University, Korea
Junqing Zhang, University of Liverpool, UK
Ji Qi, University of Amsterdam, Netherlands
Long Cheng, Dublin city university, Ireland
Mohammad Goudarzi, The University of Melbourne, Australia
Minxian Xu, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
Monowar Hasan, Wichita State University, Wichita, KS
Muhammed Tawfiqul Islam, The University of Melbourne, Australia
Kai Peng, Huaqiao University, China
Shashikant Ilager, The University of Melbourne, Australia
Shaohua Wan, Zhongnan University of Economics and Law, China
Xiaolong Xu, Nanjing University of Information Science and Technology, China
Ying Liu, University of Glasgow, UK
Yuemin Ding，Norwegian University of Science and Technology, Norway
Zhiwei Lin, Ulster University, UK
Zhi Zhou, Sun Yat-sen University, China

Call for Papers

Click here to download AiS-IoT2021 CFP in PDF

Due to the ever-increasing growth of Internet of Things (IoT) devices and applications, wireless communications, edge-cloud computing, task control and service implementations are fused together, which makes the security situations are far more complicated than ever before. Thus, a high level of security and privacy are critical for both systems and end users. The recent progress of artificial intelligent methods, e.g., deep learning, reinforcement learning, federal learning, provide fine and accurate decision makings and adaptations to address the complicated security issues of IoT systems. However, how to customize and implement the AI based techniques for security in IoTs are still under discussion. Such techniques in the security research field are still immature and inefficient.

The goal of the AiS-IoT Workshop is devoted to the most recent developments of AI based technologies for IoT security and privacy. Industry experience reports and empirical studies are also welcome. We aim to bring together researchers and practitioners to discuss the latest advancements and identify further challenges for security issues in IoTs that must be overcome. Topics of interest include, but are not limited to, the following:

The workshop welcomes submissions that cover, but are not limited to, the following topics:

• Physical Layer Securities: Securities using AI for physical layer security in 5G wireless communications, mmWave communications, THz communications, NB-IoT, Localizations, and Reconfigurable Intelligence Surfaces.
• Learning-based Approaches: General AI and machine learning theories and algorithms for IoT security applications, e.g., deep learning, reinforcement learning, federal learning, transfer learning, lifelong learning.
• Security for Platforms: IoT related platforms, including Edge Computing, Fog Computing, Cloud Computing assisted with AI methods.
• Decision Making: Intelligent decision making for potential security risks in task offloading, resource allocation, profiling, modelling, content caching.
• Hardware and SoC Designs: Special AI or security chip designs for IoT devices, gateway, middleware, accelerators, or edge computing servers.
• Performance Modelling, Evaluation and Optimization: Deep learning driven latency and energy consumption model for security issues, joint security, energy consumption and performance multi-objective optimizations.
• Security for Emerging IoT Applications: securities in Industrial IoT systems, UAV, Autonomous Vehicle, Robots and Passive Sensors equipped with wireless power transfer techniques.



Extended versions of the best papers will be considered for possible publication in .