Special Section on Optimization algorithms for Industry 4.0

Industry 4.0 is a term coined to signify the dramatic changes and revolution occurring in manufacturing process by leveraging the services of cyber-physical systems. The driving forces behind industry 4.0 revolution are big data analytics, simulation, robotics, Internet of Things (IoT), system integration both horizontally and vertically, Cloud and other forms of distributed computing paradigms, cyber security, augmented reality and additive manufacturing. Gone are the days where there was a stiff competition between companies in terms of the product manufactured. The trend has shifted in such a way that competing companies are measured or ranked based on how well they have embraced the above technologies into their manufacturing process and adapted to the evolving changes across these areas. In simple terms Industry 4.0 can be termed as computerization of Industry 3.0. Industry 4.0 provides a perfect platform in identifying business opportunities and identifying the key areas and features that may cater to these new and exciting opportunities.

Several aspects need to be considered while developing an optimized approach. It must also be noted that adapting to these technologies alone is not sufficient and by adapting to these technologies there should be a significant impact to the manufacturing process on a positive front. Most of these technologies are new and hence most of the approaches and algorithms used across these technologies are also naïve. In other words the mere adapting of these technologies into the manufacturing process alone will not yield better results, the focus should also be directed towards how these raw concepts, approaches and algorithms can be optimized to produce better results. Another aspect that needs to be considered during optimization is to have an eye on the futuristic evolutions and changes that might impact the current process. Also, the optimized algorithms should be aimed at providing robust, secure and stable and consistent results over a manufacturing period. Another aspect that needs to be contemplated is the scalability and portability of the algorithm in terms of features and volume of data that it can handle. The real challenge for several researchers is to enhance and optimize these existing approaches by considering all these aspects.

This special section provides a platform to share original ideas related to optimizing algorithms pertaining to several technologies that are driving forces for Industry 4.0. Also new ideas and approaches that enhance the performance of existing algorithms and any complexity reduction techniques which lead to performance enhancements can be shared.

Topics:

Topics of interest include:

• New techniques to enhance Industry 4.0.
• Smart manufacturing systems that promote Industry 4.0.
• Intelligent systems for smart data analytics in Industry 4.0.
• Data optimization leading to efficient manufacturing in Industry 4.0.
• Optimized algorithms that promote distributed manufacturing in Industry 4.0.
• Enhancements to privacy, security and trust in Industry 4.0 enablers.
• Big data analytics and Industry 4.0.
• Enhancement in Robotics and automation promoting Industry 4.0.
• Optimized approaches that promote distributed computing in Industry 4.0.
• Enhanced strategies and applications in Fog and Edge computing in Industry 4.0.
• Enhanced IoT devices that promote Industry 4.0.
• IoT based services and enhancements promoting Industry 4.0.
• Sophisticated simulation models for real time applications promoting smart manufacturing.
• Effective and efficient cyber physical systems that promote Industry 4.0.
• Augmented reality based services in smart and distributed manufacturing.
• Challenges and solutions in adopting Industry 4.0 across business.
• Futuristic trends of Industry 4.0.
• Designing and simulating successful manufacturing models promoting Industry 4.0.

Submission Guidelines:

Unpublished manuscripts are welcome. All submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if the paper is completely re-written or substantially (50%) extended. For additional questions please contact the guest editors. All submitted papers will be peer reviewed using the normal standards of CAEE. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue.

Before submission, authors should carefully read the journal\’s Author Guidelines available at http://www.elsevier.com/wps/find/journaldescription.cws_home/367/authorinstructions. Selected authors will be invited to submit their through the journal\’s web submission tool at evise.com/profile/#/COMPELECENG/login by selecting “SI-oai4” from the “Issues” pull-down menu during the submission process.

Schedule:

Submission of manuscript: Aug. 1, 2019

First notification: Nov. 1, 2019

Submission of revised manuscript: Dec. 20, 2019

Notification of the re-review: Jan. 10, 2020

Final notification: Mar. 6, 2020

Final paper due: Apr. 25, 2020

Publication: Aug. 2020

Guest Editors

Kelvin K.L. Wong

Senior Lecturer, School of Electrical and Electronic Engineering,

University of Adelaide, Australia

Email: kelvin.wong@adelaide.edu.au

Kelvin K. L. Wong received the B.Eng. degree (Hons.) in mechanical and production engineering from Nanyang Technological University in 2001, the M.A.I.T. degree in applied information technology from The University of Sydney in 2003, and the Ph.D. degree in electrical and electronic engineering from The University of Adelaide in 2009. From 2006 to 2009, he was doing research work on medical imaging and cardiac flow analysis. He has over ten years of medical imaging, computational hemodynamics, and simulation modeling experience. His publications now span a diverse range of topics in the medical science and engineering field.

Muhammad Imran

Associate Professor, College of Applied Computer Science,

King Saud University, Saudi Arabia. 
Email: dr.m.imran@ieee.org

Muhammad Imran is working as Associate Professor in the College of Applied Computer Science, King Saud University. He received his Ph. D in
Information Technology from University Teknologi PETRONAS, Malaysia. He
is also a Visiting Scientist at Iowa State University, USA. His research interest
includes Internet of Things, Mobile and Wireless Networks, Big Data Analytics,
Cloud computing, and Security and privacy. His research is financially
supported by several grants. He has completed a number of international
collaborative research projects with reputable universities. He has
published more than 150 research articles in top conferences and journals.


Dr. Pourya Shamsolmoali

Huawei Technologies Corporate, Shanghai, China

Institute of Image Processing & Pattern Recognition,

Shanghai Jiao Tong University, Shanghai, China.

Email: pshams@sjtu.edu.cn

Pourya Shamsolmoali, currently working as an assistant professor at Shanghai Jiao Tong  University and researcher at Huawei Technologies Corporate. Prior to that, he was Associate researcher at the Advanced Scientific Computing Division in CMCC (Euro-Mediterranean Center on Climate Change Foundation, Italy). In 2018 he selected as a young talented scientist by China ministry of education. His research activities focus on Machine learning, Image Processing, Computer Vision and Deep Learning.