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The papers in this special section explores the state-of-the-art advances in adopting blockchain technologies for the Industrial Internet of Things (IIoT). There is a growing trend of adopting blockchain technologies to the IIoT due to the traceability, nonrepudiation, and immutability of blockchain systems. The proliferation of IIoT to industrial systems is fostering the fourth industrial revolution

When IoT meets Industry 4.0, we talk about Industrial Internet of Things (IIoT) or the use of typical Internet of Things (IoT) solutions in the industrial sector, integrating different technologies such as machine learning, big data and analytics, sensor data, Machine to Machine communication (M2M) and automation. Its application within the fourth industrial revolution has initiated a profound change

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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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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In recent years, the investigations on cyber-physical systems (CPS) have become increasingly popular in both academia and industry. A primary obstruction against the booming deployment of CPS applications lies in how to process and manage large amounts of generated data for decision making. To tackle this predicament, researchers advocate the idea of coupling edge computing, or edge-cloud computing

This Editorial Submission is for SS on Deep Learning and Data Analytics to Support the Smart Grid Operation with Renewable Energy. Out of 81 submissions in SS, only 12 have been finally accepted to be published in this Transactions.

The potential for the wide-scale acceptance of the Industrial IoT is limited by a lack of automation, real-time monitoring, and connectedness. However, the future communication trend towards 5G is expected to bring greater benefits to IIoT infrastructures in terms of high-speed transmission and ultra-low latency. Furthermore, with emerging techniques such as millimeter-wave (mmWave), massive multiple-input

Intelligent ultrasound imaging based on deep learning is one of the important applications in the field of intelligent medical care. In this article, we propose an automatic fetal ultrasound standard plane recognition (FUSPR) model based on deep learning in the Industrial Internet of Things (IIoT) environment. We build a distributed ultrasound data processing and predicting platform by using the IIoT

The advent of 5G and beyond networks paves the way for significant innovations in industrial applications and services. It does not only offer broadband services through mobile networks. Indeed it unleashes application layer innovation. Potentially beneficial to millions of users. Though it looks attractive, it possesses very complex network functions proven to be well defined and consistent. This

The recent diagnosis of COVID-19 is based on real-time reverse-transcriptase polymerase chain reaction (RT-PCR) and is regarded as the gold standard for confirmation of infection. It has already been widely recognized that deep learning techniques can potentially have a substantial role in streamlining and accelerating the diagnosis of COVID-19 patients. Numerous open dataset enterprises have been

The application of Internet of Things (IoT) within industrial environments is fostering the adoption of the digital twin (DT) approach, applied at the edge of the network to handle heterogeneity stemming from siloed application management solutions and from protocols originated by different manufacturing tools and enterprise services. In this challenging context, network heterogeneity also represents

The last few decades have witnessed an explosive growth of the Internet-of-Things (IoT) systems, which provide ubiquitous sensing and computing services. When adopted in industrial and manufacturing environments, IoT is referred to as the industrial IoT (IIoT), which has attracted increasing research attention. Energy efficiency is one of the most important research topics in green IIoT, as 1) the

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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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This Special Section on ‘`Cloud-Edge Computing for Cyber-Physical Systems and Internet-of-Things’' is oriented to the dissemination of a few of those latest research and innovation results, covering many aspects of design, optimization, implementation, and evaluation of emerging cloud-edge solutions for CPS and IoT applications. The selected high-quality contributions cover a broad range of novel technologies

Identifying the anomaly location and type (fault or attack) is of paramount importance for enhancing cyber-physical situational awareness, and taking informed and effective mitigation actions in power distribution systems with an increasing number of attack points in distributed and renewable energy sources. This article proposes the fault and attack location and classification (FALCON) system to classify

Due to the stochastic and non-stationary characteristics of wind speed, the wind power generation is highly uncertain and fluctuating, which significantly challenges the operation of the power system and the associated electricity market. In this article, a new spatial-temporal method is proposed for short-term wind power prediction based on image inputs and augmented convolutional network. First,

5G communication is making much progress in achieving the Internet of Things and improving the quality of user experience in large bandwidth scenarios. By introducing a variety of new technologies, the performance of 5G has been greatly improved. However, emerging applications put forward more stringent requirements in terms of latency, reliability, peak data rate, service continuity, etc. Communication

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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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Wireless virtual reality (VR) is increasingly used in industrial Internet of Things (IIoTs). However, ultra-high viewport rendering demands and excessive terminal energy consumption restrict the application of wireless VR. Pervasive edge computing emerges as a promising method for wireless VR. In this article, we propose an energy-aware resource management scheme for wireless-VR-supported IIoTs. To

A significant amount of distributed photovoltaic (PV) generation is “invisible” to distribution system operators since it is behind the meter on customer premises and not directly monitored by the utility. The generation essentially adds an unknown varying negative demand to the system, which causes additional uncertainty in determining the total load. This uncertainty directly impacts system reliability

Extracting the charging load pattern of residential electric vehicle (REV) will help grid operators make informed decisions in terms of scheduling and demand-side response management. Due to the multistate and high-frequency characteristics of integrated residential appliances from the residential perspective, it is difficult to achieve accurate extraction of the charging load pattern. To deal with

The growing convergence among information and operation technology worlds in modern Industrial Internet of Things (IIoT) systems is posing new security challenges, requiring the adoption of novel security mechanisms involving light architectures and protocols to cope with IIoT devices resource constraints. In this article, we investigate the adoption of physically unclonable functions (PUFs) in the

Due to the limited local computation resource, industrial vehicular computation requires offloading the computation tasks with time-delay sensitive and complex demands to other intelligent devices (IDs) once the data is sensed and collected collaboratively. This article considers offloading partial computation tasks of the industrial vehicles (IVs) to multiple available IDs of the industrial mobile

In order to mitigate the periodic disturbances in voltage-source converter (VSC)-based applications, different techniques have been comprehensively investigated to mitigate these periodic disturbances, either using additional disturbance-mitigation controller or by means of modifying the modulation stage. As well as providing an in-depth analysis of various periodic disturbances in VSC-based applications

This article presents a novel integrated path-following, crash avoidance, and crash mitigation control algorithm for autonomous vehicles. To improve stability and tracking accuracy of the algorithm in extreme conditions, combined-slip tire forces are considered in the system model. A predictive control framework that monitors slip conditions at each tire is then developed to achieve good dynamics performance

Automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images can help to establish a quantitative model for diagnosis and treatment. For this reason, this article provides a new segmentation method to meet the needs of CT images processing under COVID-19 epidemic. The main steps are as follows: First, the proposed region of interest extraction implements patch mechanism strategy

In this article, a noise-suppression variable-parameter zeroing neural network (NSVPZNN) is proposed to handle the dynamic Sylvester equation. Differing from the previous zeroing neural networks (ZNNs), a new nonlinear activation function and an especially constructed time-variant parameter are developed to construct the novel NSVPZNN model. Therefore, the NSVPZNN model can achieve faster predefined-time

Industrial prognosis refers to the prediction of failures of an industrial asset based on data collected by Internet of Things sensors. Prognostic models can experience the undesired effects of concept drift, namely, the presence of nonstationary phenomena that affects the data collected over time. Consequently, fault patterns learned from data become obsolete. To overcome this issue, contextual and

The tracking performance of linear motor (LM) positioners is subject to payload uncertainty and external time-varying disturbances. Conventional robust controllers typically employ a high control gain that is substantially greater than the known a priori upper bound of the disturbance to ensure tracking error convergence. The main disadvantage of those controllers lies in that when the disturbance