About this Research Topic
The rapid evolution of Industry 4.0 is driven by the integration of advanced cyber intelligence with high-resilient, high-reliable, and high-performance networks. To support this vision, these innovations promise to enhance sustainability, yet the challenge remains to design, test, and validate foundational technologies. In particular, there is a critical need to advance communication networks, distributed collaborative computation, security detection and defense mechanisms, and machine learning models tailored to industrial environments.
Nowadays, Cyber-Physical Systems (CPSs) that underpin Industry 4.0 are becoming increasingly complex, relying on dynamic monitoring, state estimation, and feedback control through a wide range of industrial protocols, including EtherNet/IP, Modbus, and Profinet. While these advancements offer improved efficiency and performance, significant vulnerabilities remain in protecting CPSs from security threats, operational disruptions, and system faults—issues that can cause cascading failures across interconnected systems. To address these risks, it is essential to bridge the gap between information technology (IT) and operational technology (OT), ensuring that CPSs are adaptable, secure, and capable of continuous evolution.
This Research Topic aims to explore innovations in trustworthy network architectures and methodologies that support sustainable Industry 4.0 ecosystems. Key objectives include examining the role of machine learning in enhancing efficiency, security, and reliability within industrial networks, as well as addressing how to design resilient, self-healing industrial networks that are resistant to cyber threats. Additionally, we seek to evaluate quality-of-service (QoS) optimization techniques and the potential of zero-touch management in industrial environments.
To gather further insights into the boundaries of this research, we welcome articles addressing, but not limited to, the following themes:
- Technical research relating to Industry 4.0
- Resilient designs for industrial networks
- Metaverse and digital twins for networked CPSs
- Machine learning for industrial networks
- Security testing/detection/protection for industrial networks
- Zero-touch management for industrial networks
- Quality-of-service assurance/optimization for industrial networks
- Future industrial network designs
- Distributed collaborations in industrial networks
- Efficient computation offloading with industrial networks
- Wireless industrial networks
- Virtualization techniques for industrial networks
- Standard studies relating to industrial networks
Nowadays, Cyber-Physical Systems (CPSs) that underpin Industry 4.0 are becoming increasingly complex, relying on dynamic monitoring, state estimation, and feedback control through a wide range of industrial protocols, including EtherNet/IP, Modbus, and Profinet. While these advancements offer improved efficiency and performance, significant vulnerabilities remain in protecting CPSs from security threats, operational disruptions, and system faults—issues that can cause cascading failures across interconnected systems. To address these risks, it is essential to bridge the gap between information technology (IT) and operational technology (OT), ensuring that CPSs are adaptable, secure, and capable of continuous evolution.
This Research Topic aims to explore innovations in trustworthy network architectures and methodologies that support sustainable Industry 4.0 ecosystems. Key objectives include examining the role of machine learning in enhancing efficiency, security, and reliability within industrial networks, as well as addressing how to design resilient, self-healing industrial networks that are resistant to cyber threats. Additionally, we seek to evaluate quality-of-service (QoS) optimization techniques and the potential of zero-touch management in industrial environments.
To gather further insights into the boundaries of this research, we welcome articles addressing, but not limited to, the following themes:
- Technical research relating to Industry 4.0
- Resilient designs for industrial networks
- Metaverse and digital twins for networked CPSs
- Machine learning for industrial networks
- Security testing/detection/protection for industrial networks
- Zero-touch management for industrial networks
- Quality-of-service assurance/optimization for industrial networks
- Future industrial network designs
- Distributed collaborations in industrial networks
- Efficient computation offloading with industrial networks
- Wireless industrial networks
- Virtualization techniques for industrial networks
- Standard studies relating to industrial networks
Keywords: Industry 4.0, cyber networks, security, assurance, cyber-physical systems
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
