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The papers in this special section focus on softwarized networking for next generation industrial cyber-physical systems (CPSs). With the emergence of embedded and ubiquitous cyberphysical applications, the rationale of blending the physical and the virtual worlds has become ever promising. These papers examine several topics that are recently concerned in the community, including the software defined

The papers in this special section focus on the topic of green industrial Internet of Things (IIoT). These papers aim to consolidate the current state of the art in terms of fundamental research ideas and network engineering, geared toward exploiting greenness of IIoT. The IIoT is a new ecosystem that combines intelligent and autonomous machines, advanced predictive analytics, and machine–human collaboration

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The papers in this special section focus on advanced complex data analytics for smart city industrial environments. With the continuous increase in size of populations living in cities, those residents face increasing environmental pressures and infrastructure needs. To deliver a better quality of life for these residents to address these demands at a sustainable cost, smart technologies can help cities

The papers in this special section focus on configuration security for industrial automation and control systems. These systems include supervisory control and data acquisition systems, distributed control systems, and other control system configurations such as programmable logic controllers, which are typically used in industries such as electric, water and wastewater, oil and natural gas, transportation

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The papers in this special section focus on advanced signal processing and artificial intelligence (AI) technologies for industrial Big Data (IBD) powered by Industry 4.0. Modern industry has evolved from the traditional manufacturing industry to digital and intelligent industry. Huge amount of complex real-time data are generated from the thousands of industrial sensors in physical and man-made environments

The papers in this special section focus on industrial cyber-physical systems (CPS), with an emphasis on computing and communications applications. CPS systems are defined by integrating computation and communication facilities on the one hand and the monitoring and control of physical processes on the other hand. Industrial cyber–physical systems (ICPS) refer to the science and art of designing and

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This article proposes a passivity method synthesized by an adaptive neuro fuzzy inference system (ANFIS) to control a grid-tied nine-level packed E-cell (PEC9) inverter. PEC9 is an optimized components count of single-phase, single-dc-source, compact multilevel converter with an appealing feature of dc capacitors horizontal extension. Indeed, PEC9 forms a single auxiliary dc-bus to actively balance

With the increasing population of Industry 4.0, both AI and smart techniques have been applied and become hotly discussed topics in industrial cyber-physical systems (CPS). Intelligent anomaly detection for identifying cyber-physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection

This special section aims at soliciting original research and practical contributions from both industry and academia to advance the IIoT, including network modeling and architecture, AI algorithms for various layers, intelligent resource management, big data driven edge systems, orchestration of edge, and cloud servers. Through a rigorous peer-review process, nine articles have been accepted. In the

Industrial investments into distributed energy resource technologies are increasing and playing a pivotal role in the global transactive energy, as part of a wider drive to provide a clean and stable source of energy. The management of prosumers, which consume and as well as generate energy, with heterogeneous energy sources is critical for sustainable and efficient energy trading procedures. This

Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and cloud computing. To handle the workflows from Industrial Cyber-Physical systems, more and more data centers have been built across the globe to serve the growing needs of computing and storage. This has led to an enormous increase in energy usage by cloud data centers

In softwarized industrial networking, honeypot identification is very important for both the attacker and the defender. Existing honeypot identification relies on simple features of honeypot. There exist two challenges: The simple feature is easily simulated, which causes inaccurate results, whereas the advanced feature relies on high interactions, which lead to security risks. To cope with these challenges

In the context of new retail and personalized, small-batch, distributed collaborative production, orders arrive in real time, and each workshop needs to organize production lines based on suborders in real time under the constraints of smart contracts. However, the existing cloud centralized scheduling method has very high calculation and communication costs when inserting orders in real time, and

Industry 5.0 is the digitalization, automation, and data exchange of industrial processes that involve artificial intelligence, industrial Internet of Things (IIoT), and industrial cyber-physical systems (I-CPS). In healthcare, I-CPS enables the intelligent wearable devices to gather data from the real-world and transmit to the virtual world for decision-making. I-CPS makes our lives comfortable with

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Future industrial cyber-physical system (CPS) devices are expected to request a large amount of delay-sensitive services that need to be processed at the edge of a network. Due to limited resources, service placement at the edge of the cloud has attracted significant attention. Although there are many methods of design schemes, the service placement problem in industrial CPS has not been well studied

With the recent development of Internet of Things (IoT) in the next generation cyber-physical system (CPS) such as autonomous driving, there is a significant requirement of big data analysis with high accuracy and low latency. For efficient big data analysis, deep learning (DL) supports strong analytic capability; it has been applied at the cloud and edge layers by extensive research to provide accurate

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In the current industrial revolution, additive manufacturing (AM) embodies a promising technology that can enhance the effectiveness, adaptability, and competitiveness of supply chains (SCs). Moreover, it facilitates the development of distributed SCs, thereby enhancing product availability, inventory levels, and lead time. However, the wide adoption of AM in industrial SCs creates various challenges

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Internet of Things (IoT) has become one of the fastest-growing technologies and has been broadly applied in various fields. IoT networks contain millions of devices with the capability of interacting with each other and providing functionalities that were never available to us before. These IoT networks are designed to provide friendly and intelligent operations through big data analysis of information

Android as an operating system is now increasingly being adopted in industrial information systems, especially with cyber–physical systems (CPS). This also puts Android devices onto the front line of handling security-related data and conducting sensitive behaviors, which could be misused by the increasing number of polymorphic and metamorphic malicious applications targeting the platform. The existence

With the proliferation of information and communication technology in every walks of the society, including healthcare services, digitization, and increased sophistication have been gaining pace, digital healthcare alternatives such as electronic healthcare record (EHR) have gained prominence with increased patients’ data volume. However, traditional EHR-based systems are plagued by data loss risks

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using artificial intelligence within the cyber–physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices’ authenticity. The ability

Industrial wireless networks (IWNs) have attracted significant attention for providing time-critical delivery services, which can benefit from device-to-device (D2D) communication for low transmission delay. In this article, a distributed scheduling problem is investigated for D2D-enabled IWNs, where D2D links have various age-of-information (AoI) constraints for information freshness. This problem

5G networks are expected to provide cost-efficient, reliable, and flexible services for industrial productions and applications potentially, by introducing emerging network technologies like blockchain and network functions virtualization (NFV), which virtualizes network functions and runs them on standard infrastructure rather than customized hardware. However, how to deal with the emerging security

Smart cities endeavor to deliver safe and sustainable infrastructure services that enable individuals, organizations, and communities alike to be productive, healthy, informed, and actively involved in rapid urbanization. The widespread installation of closed-circuit television cameras and continuously generated video streams are a strategic data source that can contribute toward safety and sustainability

Intelligent anomaly detection for key performance indicators (KPIs) is important for keeping services reliable in industrial-based cyber–physical systems (CPS). However, it is common in practice for various KPI sampling strategies to be utilized. We experimentally verify that anomaly detection is highly sensitive to irregular sampling, and accordingly go on to investigate low-cost anomaly detection

The papers in this special section focus on applications of artificial intelligence in industrial power electronics and systems. The grid infrastructures and modernization, as well as the integration of renewable energies and using smart meters, can generate a large amount of data. These can lead to high complexity in the power system/electronics operation and control. Moreover, grid contingencies

Many industrial-Internet-of-Things applications require autonomous operation and incorporate devices in inaccessible locations. Recent advances in wireless power transfer (WPT) and autonomous vehicle technologies, in combination, have the potential to solve a number of residual problems concerning the maintenance of, and data collection from embedded devices. Equipping inexpensive unmanned aerial vehicles

The labeled monitoring data collected from the electromechanical system is limited in the real industries; traditional intelligent fault diagnosis methods cannot achieve satisfactory accurate diagnosis results. To deal with this problem, an intelligent fault diagnosis method for electromechanical system based on a new semisupervised graph convolution deep belief network algorithm is proposed in this

Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial environment to achieve Industry 4.0 benefits. In this article, we consider a new architecture of digital twin (DT) empowered Industrial IoT, where DTs capture the characteristics of industrial devices to assist federated learning. Noticing that DTs may bring estimation deviations

This article proposes a unified solution to address the energy issues in net-zero energy building (ZEB), as a new contribution to earlier studies. The multicarrier energy system, including hydro-wind-solar-hydrogen-methane-carbon dioxide-thermal energies is integrated and modeled in ZEB. The electrical sector is supplied by hydro-wind-solar, combined heat and power (CHP), and pumped hydro storage (PHS)

In this article, suffering from unmatched visual-servo uncertainties and unknown dynamics/disturbances, an extreme learning-based monocular visual-servo (ELMVS) scheme is developed for maneuvering an unmanned surface vessel (USV) to reach the desired pose. By virtue of the backstepping philosophy, complex visual-servo unknowns are elaborately encapsulated into lumped nonlinearities, which are further

The distribution networks can convincingly break down into small-scale self-controllable areas, namely microgrids (μG), to substitute μGs arrangements for effectively coping with perturbations. This flexible structure not only could potentially possess the strength to recover quickly, but also ensures the supply of vital loads and preserves functionalities under any contingency. To achieve these targets

Fifth generation and beyond networks are envisioned to support multi industrial Internet of Things (IIoT) applications with a diverse quality-of-service (QoS) requirements. Network slicing is recognized as a flagship technology that enables IIoT networks with multiservices and resource requirements by allowing the network-as-infrastructure transition to the network-as-service. Motivated by the increasing