Special Issue on AI-Powered Automated Industrial Internet of Things and Industry 4.0
Published 26 March, 2021
The industrial sector is rapidly moving towards smart and autonomous industrial processes, without human intervention. This transformation is mainly driven by the recent emerging technology, the Industrial Internet of Things (IIoT). In addition, Industry 4.0 is being adopted in several applications and services for manufacturing purposes. IIoT and Industry 4.0 interconnect heterogeneous sensor networks, robots, actuators, machines and devices, which are then used through different industrial verticals, including transportation, supply chain, healthcare, blockchain and digital twin, to name but a few. This generates a huge volume of data, which is typically transmitted to either edge nodes, for time-critical applications, or the central cloud for further processing and inferencing.
Artificial intelligence (AI), particularly deep/machine learning techniques, offer promising solutions to curate and analyze the massive sensed data. However, deploying deep/machine learning techniques gives rise to several challenges around latency, reliability and security. It remains unclear how industrial systems will support AI to optimize their main operations. In this special issue, we bring together researchers from academia and industry to explore studies on the integration of AI techniques with industrial systems, adopting Industry 4.0 and IIoT solutions.
Topics Covered:
- Deep learning and neural networks for Industry 4.0 and IIoT
- Statistical modelling and data mining for Industry 4.0 and IIoT
- Real-time and predictive analytics for Industry 4.0 and IIoT
- New AI-based architectures for Industry 4.0 and IIoT
- New models, concepts and frameworks supporting AI for Industry 4.0 and IIoT
- AI-empowered management for Industry 4.0 and IIoT
- Edge-based deep learning models for Industry 4.0 and IIoT
- Scalable distributed learning for Industry 4.0 and IIoT (e.g., federated learning)
- Computation, communication and energy-efficient machine learning for Industry 4.0 and IIoT
- Security issues in AI-enabled Industry 4.0 and IIoT
- AI-based blockchain for Industry 4.0 and IIoT
- AI-based digital twins for Industry 4.0 and IIoT
- Privacy-aware AI-based models for Industry 4.0 and IIoT
Important Deadlines:
- Submission deadline: 15 September 2021
Submission Instructions:
Please read the Guide for Authors before submitting. All articles should be submitted online; please select SI: Automated Industrial IoT and Industry 4.0 on submission.
Guest Editors:
- Managing Guest Editor: Dr. Bouziane Brik, Bourgogne University, France. Email: bouziane.brik@u-bourgogne.fr
- Dr. Zhipeng Cai, Georgia State University, USA. Email: zhipeng.cai@gmail.com
- Dr. Jun Shen, University of Wollongong, Australia. Email: jshen@uow.edu.au
- Dr. Dingde Jiang, University of Electronic Science and Technology of China. Email: jiangdd@uestc.edu.cn
- Dr. Moayad Aloqaily, xAnalytics Inc., Canada; Al Ain University, United Arab Emirates. Email: mzaloqaily@gmail.com
The industrial sector is rapidly moving towards smart and autonomous industrial processes, without human intervention. This transformation is mainly driven by the recent emerging technology, the Industrial Internet of Things (IIoT). In addition, Industry 4.0 is being adopted in several applications and services for manufacturing purposes. IIoT and Industry 4.0 interconnect heterogeneous sensor networks, robots, actuators, machines and devices, which are then used through different industrial verticals, including transportation, supply chain, healthcare, blockchain and digital twin, to name but a few. This generates a huge volume of data, which is typically transmitted to either edge nodes, for time-critical applications, or the central cloud for further processing and inferencing.
Artificial intelligence (AI), particularly deep/machine learning techniques, offer promising solutions to curate and analyze the massive sensed data. However, deploying deep/machine learning techniques gives rise to several challenges around latency, reliability and security. It remains unclear how industrial systems will support AI to optimize their main operations. In this special issue, we bring together researchers from academia and industry to explore studies on the integration of AI techniques with industrial systems, adopting Industry 4.0 and IIoT solutions.
Topics Covered:
- Deep learning and neural networks for Industry 4.0 and IIoT
- Statistical modelling and data mining for Industry 4.0 and IIoT
- Real-time and predictive analytics for Industry 4.0 and IIoT
- New AI-based architectures for Industry 4.0 and IIoT
- New models, concepts and frameworks supporting AI for Industry 4.0 and IIoT
- AI-empowered management for Industry 4.0 and IIoT
- Edge-based deep learning models for Industry 4.0 and IIoT
- Scalable distributed learning for Industry 4.0 and IIoT (e.g., federated learning)
- Computation, communication and energy-efficient machine learning for Industry 4.0 and IIoT
- Security issues in AI-enabled Industry 4.0 and IIoT
- AI-based blockchain for Industry 4.0 and IIoT
- AI-based digital twins for Industry 4.0 and IIoT
- Privacy-aware AI-based models for Industry 4.0 and IIoT
Important Deadlines:
- Submission deadline: 15 September 2021
Submission Instructions:
Please read the Guide for Authors before submitting. All articles should be submitted online; please select SI: Automated Industrial IoT and Industry 4.0 on submission.
Guest Editors:
- Managing Guest Editor: Dr. Bouziane Brik, Bourgogne University, France. Email: bouziane.brik@u-bourgogne.fr
- Dr. Zhipeng Cai, Georgia State University, USA. Email: zhipeng.cai@gmail.com
- Dr. Jun Shen, University of Wollongong, Australia. Email: jshen@uow.edu.au
- Dr. Dingde Jiang, University of Electronic Science and Technology of China. Email: jiangdd@uestc.edu.cn
- Dr. Moayad Aloqaily, xAnalytics Inc., Canada; Al Ain University, United Arab Emirates. Email: mzaloqaily@gmail.com