Data-Driven Collaborative Engineering

  in Special Issue   Posted on August 2, 2019

Information for the Special Issue

Submission Deadline: Thu 31 Oct 2019
Journal Impact Factor : 2.680
Journal Name : Advanced Engineering Informatics
Journal Publisher:
Website for the Special Issue:
Journal & Submission Website:

Special Issue Call for Papers:

Collaborative Engineering (CE) involves the research and development of collaboration technologies and their applications to the design of processes, products, systems, and services in industries and societies with objectives for better product quality, shorter lead-time, more competitive cost and higher customer satisfaction. Collaboration technologies include theories, methods, mechanisms, protocols, software tools, platforms, and services that support communication, coordination and collaboration among people, software and hardware systems. New generation of ICT technologies, including Cloud/Fog/Edge computing, Internet of Things (IoT), Big Data and Artificial Intelligence/Deep Learning, can substantially expand the frontiers of what is possible in the CE area.

Data is the new oil. In the real-world, the pervasive sensing ability of IoT systems gives rise to the generation of huge and diverse volumes of data, which can be utilized to assist optimal decision-making for collaboration technologies and their applications. The data sets are still growing rapidly because the density of sensing and actuation coverage is still at early stages of development and much more IoT devices will be deployed. Cloud/Fog/Edge computing provides different but complementary paradigms for data storage, integration and processing. Big Data and Artificial Intelligence/Deep Learning enable the efficient data analytics for useful information, insights and knowledge. With the help of ICT, physical objects are virtualized and represented as twin models (Avatars) seamlessly and closely integrated in both the physical and cyber spaces. The simulation systems which comprise twin models and other digital models will operate as an essential part of the corresponding physical system to assist decision-making, training, etc. As a result, what has been described as a data-driven revolution has reshaped both CE and scientific landscapes.

Specific topics of interest include, but are not limited to the following:

  • Collaboration theory, ontology and methodology
  • Architecture, interoperability and standard for CE
  • Emerging sensing technologies of IoT for CE
  • IoT-enabled collaborative process monitoring and control
  • Resource composition and reliable networked application
  • Cloud based collaborative Big Data and Artificial Intelligence
  • Human-centered pervasive CE environment
  • Augmented reality and wearable computing for CE
  • Collective design and social manufacturing in CE
  • Cybersecurity and privacy for CE systems
  • Cyber physical production systems and industry 4.0

Proposed Schedule

  • Paper submission: October 31, 2019
  • First review results: January 31, 2020
  • Revised paper submission: March 31, 2020
  • Second review results: May 31, 2020
  • Notification of final decision: June 30, 2020

Submission of Paper

All papers forwarded for the special issue must use the new online submission system EVISE® for Advanced Engineering Informatics ( To ensure that your paper is correctly identified for inclusion into the special issue review, it is important that you select \”Data-Driven Collab Eng\” when you reach the \”Article Type\” step of the submission process.

Manuscripts should be prepared in accordance with the format and guidelines found at Submitted papers should not have been previously published nor currently under consideration for publication elsewhere.

Guest Editors

Weiming Shen, Professor
State Key Lab of Digital Manufacturing Equipment and Technology
Huazhong University of Science & Technology, Wuhan 430074, China

Chen Yang, Associate Professor
School of Computer Science and Technology
Beijing Institute of Technology, China
5 Zhongguancun South Street, Beijing 100081, China

Shulin Lan, Assistant Professor
School of Economics and Management
University of Chinese Academy of Sciences, China
80 Zhongguancun East Road, Beijing 100190, China