Honolulu, Hawaii, USA, August 6, 2020
|Paper submission:||March 29, 2020|
|Notification of acceptance:||April 27, 2020|
|Camera-ready paper due:||May 11, 2020|
|Workshop date:||August 6, 2020|
Huaming Wu, Tianjin University, China email@example.com
Zhi Zhou, Sun Yat-sen University, China firstname.lastname@example.org
Kaitai Liang, University of Surrey, United Kingdom email@example.com
Xiaolong Xu, Nanjing University of Information Science and Technology, China
Adel Nadjaran Toosi, Monash University, Australia
Bin Liu, University of Strathclyde, UK
Bing Lin, Fujian Normal University, China
Guang Peng, Free University of Berlin, Germany
Haneul Ko, Korea University, Korea
Hao Qian, Kansas State University, United States
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
Mohammadreza Razian, Iran University of Science and Technology, Iran
Minxian Xu, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
Muhammed Tawfiqul Islam, The University of Melbourne, Australia
Kai Peng, Huaqiao University, China
Shashikant Ilager, The University of Melbourne, Australia
Sukhpal Singh Gill, Queen Mary University of London, UK
Shaohua Wan, Zhongnan University of Economics and Law, China
Xiaofei Wang, Tianjin University, China
Yingjun Deng, Tianjin University, China
Ying Liu, University of Glasgow, UK
Yubin Zhao, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
Yuemin Ding, Norwegian University of Science and Technology, Norway
Yuan Yang, Southeast University, China
Zhiwei Lin, Ulster University, UK
Click here to download IOIoT 2020 CFP in PDF
Fog computing or edge computing emerges as a novel computing paradigm that harnesses computing resources in the proximity of the Internet of Things (IoT) devices, alongside with cloud computing, provide services in a timely manner. Deep Neural Networks (DNNs) have been widely used in IoT applications. However, due to the ever-increasing growth of IoT devices with resource-hungry applications and the unprecedented demands of computing capabilities, edge servers with limited resources cannot efficiently satisfy the requirements of the IoT applications and DNN computing. Task offloading technology can break through the resource limitations of IoT devices, reduce the computing load and improve the task processing efficiency. Optimizing DNNs through task offloading has become a new direction in edge intelligence research. DNN-based applications can be partitioned, with some layers being calculated on the IoT side and others on the edge/cloud server. Dynamic application partitioning depends on factors such as hardware platform, wireless network, and workload. Therefore, in order to reduce the latency and energy consumption, more intelligent technologies are required to address such complicated scenarios.
Due to the ever-increasing growth of Internet of Things (IoT) devices with resource-hungry applications and the unprecedented demands of computing capabilities, edge servers with limited resources cannot efficiently satisfy the requirements of the IoT applications and Deep Neural Networks (DNNs) computing. Task offloading technology can break through the resource limitations of IoT devices, reduce the computing load and improve the task processing efficiency. Optimizing DNNs through task offloading has become a new direction in edge intelligence research. Deep learning driven approaches can facilitate offloading decision making, dynamic resource allocation and content caching, benefit in coping with the growth in volumes of communication and computation for emerging IoT applications. However, how to customize deep learning techniques for task offloading in IoTs is still under discussion. Learning algorithms in edge computing are still immature and inefficient.
The goal of the IOIoT Workshop is devoted to the most recent developments of intelligent offloading technologies for IoT and and edge computing. 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 task offloading in IoTs that must be overcome.
The workshop welcomes submissions that cover, but are not limited to, the following topics:
Extended versions of the best papers will be considered for possible publication in Journal of Systems Architecture（CCF: B)，Security and Communication Networks（CCF: C) and Journal of Cloud Computing（IF: 2.14).
The proceeding is available here.