IOIoT 2020

Important Dates

Organizing Committee

General Chairs:
Huaming Wu, Tianjin University, China whming@tju.edu.cn
Zhi Zhou, Sun Yat-sen University, China zhouzhi9@mail.sysu.edu.cn

TPC Chairs:
Kaitai Liang, University of Surrey, United Kingdom k.liang@surrey.ac.uk
Xiaolong Xu, Nanjing University of Information Science and Technology, China
xlxu@nuist.edu.cn

Program Committee

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

Call for Papers

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:

• Computing Paradigm Frontiers: Edge Computing, Fog Computing, Cloud Computing that assisted with AI
• Theories, Scenarios and Applications: Learning-based approaches for
• Learning-based approaches: Intelligent decision making for task offloading, resource allocation, profiling, modelling, content caching, cyber-security, and privacy in IoTs
• Design, Development and Architecture: New AI-based theories, scenarios and applications for application placement, computation offloading or data offloading in IoTs
• Performance Modelling and Evaluation: Deep learning driven latency and energy consumption model of task offloading; Joint communication and computation optimization for emerging IoT applications
• Optimization Algorithms: Joint communication and computation optimization for emerging IoT applications
• Sustainable and Green Computing: Intelligent resource management and scheduling; DNN-based applications partitioning in IoTs



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).

Instructions for Authors:

• Submitted manuscripts must be formatted in standard IEEE camera-ready format (double-column, 10-pt font) and must be submitted via easychair as PDF files (formatted for 8.5x11-inch paper). The manuscripts should be no longer than 6 pages. Two additional pages are permitted if the authors are willing to pay an over-length charge at the time of publication (manuscripts should not exceed 8 pages). Submitted papers cannot have been previously published in or be under consideration for publication in another journal or conference. The workshop Program Committee reserves the right to not review papers that either exceeds the length specification or have been submitted or published elsewhere. Submissions must include a title, abstract, keywords, author(s) and affiliation(s) with postal and e-mail address(es).
• Submitted papers will be reviewed by the workshop Program Committee and judged on originality, technical correctness, relevance, and quality of presentation. An accepted paper must be presented at the ICCCN 2020 venue by one of the authors registered at the full registration rate. Each workshop registration covers up to two workshop papers by an author. Accepted and presented papers will be published in the ICCCN proceedings and submitted to IEEE Xplore as well as other Abstracting and Indexing (A&I) databases. IEEE reserves the right to exclude a paper from distribution after the conference, including IEEE Xplore® Digital Library if the paper is not presented by the author at the conference.
• Electronic submission site: https://easychair.org/conferences/?conf=icccn2020 .

ICCCN 2020 Paper Submission Terms and Conditions:

• Authors declare that the submission is original and has not been submitted to other venue or under consideration by other venues.

• Paper titles and/or author names cannot be changed and/or added to the papers once papers are submitted to ICCCN 2020 for review and in the final camera-ready manuscript.

• If the paper is accepted, at least one of the authors must register at full rate and present it in person at the conference. Accepted and paid paper(s) but not presented onsite by the registered author(s) (for any reason, including visa issues, travel problems, etc.) will be published in the conference proceedings only. We strongly encourage that all authors of accepted papers apply for an entry visa to Hawaii in case one author cannot get the entry visa.

Keynote Speakers

List of Accepted Papers

The proceeding is available here.

Program

Time	August 3rd

Registration

Please note that at least one author of each accepted paper needs to register and present the paper at the workshop.

Please register: here

Venue

• Please refer to ICCCN 2020 for hotel and travel information.

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