The development of cloud computing, big data, Internet of Things, high-performance computing, and other emerging technologies is dramatically influencing industrial applications. Specifically, the current scenario is usually an ecosystem made up of intelligent embedded systems and intelligent products. It combines lots of intelligent and autonomous devices, which can perform predictive analysis and human-machine collaboration, to improve the level of personalization, efficiency and reliability. With the expansion of the industrial system, the complexity of the system has also increased considerably. Centralized data centers cannot analyze such massive amounts of data in a timely fashion. Thus, the concept of edge computing is presented to solve the issue.
With edge computing and analytics, the data is processed near the source, in sensors, controllers, machines, and gateways. In addition to the basic requirements for network latency, industrial applications also place requirements on self-monitoring, self-diagnosis, self-reconfiguration, and self-adaptation. This requires the edge not only to perform simple data processing, but also integrate intelligence. Therefore, the future trend is to integrate new intelligence algorithms into the edge, but research on this is still limited. The purpose of this special section is to present the advances in the field of edge-based industrial applications, to promote the development of the theory and practice in this area.
This special section has a wide scope that includes the development of edge intelligence, current applications, analysis of key technologies, and methods to solve problems for future development of edge intelligence.
Multi-agent systems in industry
Control with distributed edge intelligence
Data interaction between edge and cloud platform
Security and privacy in edge intelligence
Human and machine intelligence fusion based on edge computing
Intelligent decision-making systems for edge computing
Edge intelligence for Industrial Internet of Things
Data mining and knowledge discovery based on big data analysis from the aspect of edge intelligence
Blockchain technology for edge intelligence
Digital twins and digital shadows from the aspect of edge intelligence
Mixed production planning and scheduling based on edge intelligence
All submissions must not be currently under review for publication elsewhere. All articles will be peer-reviewed and accepted based on quality, originality, novelty, and relevance to the special section’s theme. Authors are invited to submit their papers through the journal’s web submission tool at https://www.evise.com/profile/#/COMPELECENG/login by selecting “SI-eiia” from the “Issues” pull-down menu during the submission process.
Before submission, authors should carefully read the journal’s Author Guidelines available at http://www.elsevier.com/wps/find/journaldescription.cws_home/367/authorinstructions
Submission of manuscript: July 15, 2020
First notification: August 31, 2020
Submission of revised manuscript: September 30, 2020
Notification of the re-review: October 31, 2020
Final notification: November 15, 2020
Final paper due: December 15, 2020
Publication: March 2021
Main Guest Editor: Jiafu Wan, South China University of Technology, China, firstname.lastname@example.org
Guest Editor: Fangyang Shen, New York City College of Technology, USA, email@example.com
Jiafu Wan has been a Professor in School of Mechanical & Automotive Engineering at South China University of Technology since Sep 2015. He has directed 20 research projects, including the National Key Research and Development Program of China, the Key Program of National Natural Science Foundation of China, and Guangdong Province Key Areas R & D Program. Thus far, he has published more than 150 scientific papers, including 100+ SCI-indexed papers, 40+ IEEE Trans./Journal papers, 20 ESI Highly Cited Papers and 4 ESI Hot Papers. According to Google Scholar Citations, his published work has been cited more than 9700 times (H-index = 46). His SCI other citations (sum of times cited without self-citations) reached 2500 times (H-index = 33) according to Web of Science Core Collection. He is an Associate Editor of IEEE/ASME Transactions on Mechatronics, Journal of Intelligent Manufacturing, and Computers & Electrical Engineering, and Editorial Board for Computer Integrated Manufacturing Systems. His research interests include Cyber-Physical Systems, Intelligent Manufacturing, Big Data Analytics, Industry 4.0, Smart Factory and Cloud Robotics.
Fangyang Shen received his Ph.D. in Computer Science from Auburn University in Alabama, USA. He is currently working as an associate professor in Department of Computer Systems Technology at New York City College of Technology (CUNY). He has over twenty years’ research and teaching experience in wireless networks, high performance computing and STEM education. For academic services, Dr. Shen has been a conference chair and TPC for more than one hundred international conferences. He is also an associate editor, lead guest editor for multiple Journals, such as Journal of Parallel Computing and Computer and Electrical Engineering. In addition, he has leaded multiple large NSF Noyce projects for over three million dollars and has four years’ industrial experience in information technology.