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Special Session on
Intelligent Edge Computing for Industrial IoT
(in conjunction with IECON 2020)
46th Annual Conference of the IEEE Industrial Electronics Society (IES)
18-21 October 2020
(Supported by IEEE IES Technical Committee on Cloud and Wireless Systems for Industrial Applications)
IECON 2020 is the 46th Annual Conference of the IEEE Industrial Electronics Society (IES), focusing on contemporary industry topics ranging from electronics, controls, manufacturing, to communications and computational intelligence.
IECON is the flagship annual conference of IES and returns to Singapore for the first time after 1988. It aims to create a forum for scientists and practising engineers throughout the world to present the latest research findings and ideas in the areas of Industrial Electronics, and possible contributions toward sustainable development and environment preservation.
Scope of the Session
Academics and industry experts are now advocating for going from large-centralized cloud computing infrastructures to computing nodes located at the edge of the network in industrial Internet of Things (IIoT). However, many fundamental questions arise at the same time. For example, how and where to process the learning data are significant issues due to the resource constraints for processing of the raw data at the master and controlled domain. Therefore, edge learning that is being actively investigated by many researchers in several technologies is yet to be applied to intelligent edge computing support engines. Moreover, the selection of a learning algorithm will become a significant factor to be considered. The goal of the workshop is to provide a forum for scientists, engineers and researchers to discuss and exchange novel ideas, results, experiences and work-in-progress on all aspects of intelligent edge computing systems for IIoT.
- Edge, fog, and mobile edge computing for Industrial IoT
- Machine learning and computational intelligence for handling big data in Industrial applications
- Information-centric networking and software-defined network for edge intelligence
- Intelligent edge-based mobile computing and analysis
- Multimedia QoS, and traffic management in industrial networks
- Real-time communication interfaces and protocols
- Intelligent infrastructures at the edge
- Hardware testbed or field trial for AI-driven intelligent edge computing for industrial applications
- Security and related considerations in intelligent edge computing
Prospective authors are invited to electronically submit full regular papers of their work using the link above. English is the working language of the conference. Regular and Special Session papers are limited to six (6) pages of A4 or letter format. An additional 2 pages can be included at extra fee.
Authors must adhere to the IEEE Conference paper format. Draft version of papers should be submitted for review before the submission deadline (see important dates).
IEEE is strict about the requirements for PDF files for inclusion in the IEEE Xplore? Digital Library. It is strongly recommended to use the set of templates in MS Word and LaTeX formats provided by IEEE. Please use them to create your paper (please do not modify the style or the format). To learn more about formatting of papers for IEEE IES conferences, please visit submission page.
Please make sure that only original work is being submitted (not previously published/copyrighted). The conference will be using the CrossCheck automated screening system to verify the originality of papers. IEEE takes disciplinary actions when violations are detected.
Before submission, please familiarise yourself with the following IEEE authorship guidelines and policies.
select SS-70: Intelligent Edge Computing for Industrial IoT
Deadline for submission of special session papers: June 9 2020
Notification of acceptance: July 4 2020
Camera ready manuscripts: August 1 2020
Mithun Mukherjee Guangdong University of Petrochemical Technology, Maoming, China
Shiny Abraham Seattle University, Seattle, USA
Jaime Lloret University of Valencia, Spain