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The development and evolution of modern information and communication technologies is leading us to the fourth industrial revolution, in which the Industrial Internet of Things (IIoT) is assumed to be one of the key aspects to realize Industry 4.0. Federated learning facilitates the implementation of secure platform with consideration on data privacy to support IIoT. Many researchers and practitioners

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Nowadays, a new intelligence structure known as the Artificial Intelligence of Things (AIoT) comes into play. Broadly speaking, AIoT is a fusion of Artificial Intelligence (AI) and Internet of Things (IoT) in practical applications. It applies AI to the edge and gives devices the ability to understand the data, observe the environment around them, and decide what to do best. However, the link among

This is the editorial of the SS entitled ‘`Digital Twinning: Integrating AI-ML and Big Data Analytics for Virtual Representation’'.

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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

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Mobile target tracking with artificial intelligence (AI) approaches such as deep reinforcement learning (DRL) in edge-assisted Internet of Things (Edge-IoT) platform can be promising. In this article, we propose DRLTrack , a framework for target tracking with a collaborative DRL called C-DRL in Edge-IoT with the aim to obtain two major objectives: high quality of tracking (QoT) and resource-efficient

The statistical value of latencies between two sets of hosts over a given period, which is referred as to the statistical latency, can benefit many applications in the next-generation networks, for example, Network-in-a-Box-based resource provisioning. However, the existing methods can hardly achieve low measurement cost and high prediction accuracy simultaneously in large-scale scenarios. In this

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With the prevalence of smart appliances, smart meters, and Internet of Things (IoT) devices in smart grids, artificial intelligence (AI) built on the rich IoT big data enables various energy data analysis applications and brings intelligent and personalized energy services for users. In conventional AI of Things (AIoT) paradigms, a wealth of individual energy data distributed across users’ IoT devices

The evolution of artificial intelligence and Internet of Things (IoT) envision a highly integrated artificial IoT (AIoT) network. Flocking and cooperation with multiple unmanned aerial vehicles (UAVs) are expected to play a vital role in industrial AIoT networks. In this article, we formulate the collision-free flocking problem of fixed-wing UAVs as a Markov decision process and solve it in the deep

While the past 50 years have been dominated by the so-called communication revolution, today, we are at the dawn of the energy revolution. The entire economy is energy driven, being electricity its most important energy vector. By the end of 2040, electricity is expected to contribute up to 40% of the increase in final energy consumption, and concepts, such as smart grid or internet of energy, are

Nowadays, users can personalize Internet of Things (IoT) devices/web services via trigger-action programming (TAP). As the number of connected entities grows, the relations of triggers and actions become progressively complex (i.e., the heterogeneity of TAP), which becomes a challenge for existing models to completely preserve the heterogeneous data and semantic information in trigger and action. To

The emergence of Industry 4.0 and the rapid advances in the Industrial Internet of Things (IIoT) have provided manufacturers with the ability to remotely monitor the process by deploying automatic fault detection in an IoT-based predictive maintenance system. However, the monitoring targets are now manufacturing plant-wide instead of being just a local area. Multiple types of faults are involved and

These days, hybrid nature-inspired population-based intelligent optimization methods are a wide range of the algorithms, which are used very often to solve real-world (industrial) optimizationproblems. As it was shown in [item 1) in the Appendix] by Slowik and Cpalka, nature-inspired methods can be divided into several groups of algorithms. In these groups of nature-inspired algorithms, we can find

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Pepper is a humanoid robot capable of expressing body language, perceiving, and interacting with its surrounding environment, thanks to a wide set of sensors and actuators and exposing capabilities and high-level interfaces for natural interaction with humans. In this article, we present the development of VPepper, the Pepper virtual replica, by describing experiences focused on the interaction of

Digital twin is the innovation backbone of the smart manufacturing by delivering virtual representation of the real world. Aiming at constructing virtual representations of visual scenes, scene graph generation is a digital twin task that not only models objects but also infers their relationships. Existing works usually learn coarse global context when predicting relationships leading to excessive

The site selection of sports facilities is a pivotal link in the construction of city livable environment and the development of sports business in digital-twinning cities. Recent years have witnessed data mining and visualization technologies bringing the convenience as well as opportunities for intelligent site selection. However, the lack of effective and reliable systematic analysis leads to difficulties

Advances in information and communication technologies have significantly influenced the operation of low-voltage distribution grids. As essential elements of distribution grids, user-side smart meters find many smart grid applications, for example to measure electrical energy use and facilitate communications. However, the service models of distribution grids remain under development in association

This Special Section presents the latest developments on intelligent modeling, neural networks, deep learning, and adaptive estimation, and their applications in industrial applications. Through a rigorous peer-review process, eleven articles have been accepted, which are summarized below.

Technological advancements of urban informatics and vehicular intelligence have enabled connected smart vehicles as pervasive edge computing platforms for a plethora of powerful applications. However, varies types of smart vehicles with distinct capacities, diverse applications with different resource demands as well as unpredictive vehicular topology, pose significant challenges on realizing efficient

The variability and uncertainty of wind power generation pose severe challenges in balancing supply and demand in real time. To overcome this challenge, one promising solution is to exploit the self-regulation capabilities of wind turbines (WTs) and operate them as “semi-dispatchable” resources. In this article, the rotor speed control and pitch angle control are utilized simultaneously to regulate

To support cyber security assessment and enhancement of cyber-physical systems, a cross-layered experimental prototype platform has been developed. In this article, to make the prototype versatile, a modular approach has been taken. There are in total four modules: 1) Attack scenario generation; 2) security enhancement; 3) security evaluation; and 4) platform management. In reference to typical cyber-enabled

Sentiment classification of web social media faces the problem of text context semantics missing. The existing research mainly solves the problem of text context semantic missing by mining language symbol information in web social media text, seldom considering the emoticon symbols and punctuation symbols in web social media text. Similar to language symbols, emoticons’ symbols and punctuation symbols

Domestic waste classification was incorporated into legal provisions recently in China. However, relying on manpower to detect and classify domestic waste is highly inefficient. To that end, in this article, we propose a multimodel cascaded convolutional neural network (MCCNN) for domestic waste image detection and classification. MCCNN combined three subnetworks (DSSD, YOLOv4, and Faster-RCNN) to

In this article, to guarantee the good tracking performance of the precision motion system for various tracking tasks, an online iterative learning compensation method is proposed for closed-loop motion control systems. The prediction model is based on the closed-loop model of the linear second-order system with a proportional-integral-derivative controller, and an estimation term is added to deal

Electroencephalogram (EEG) signal classification is used in many applications. Typically, this classification is implemented based on methods which consist of two steps. These steps are known as the step of signal preprocessing and the step of the classification. The signal preprocessing step transforms initial signal into classification attributes. According to several studies, this transformation

This article aims to realize a cooperative active power control of doubly fed induction generator (DFIG)-based wind farm (WF) to maximize the total reactive power capacity while maintaining the active power supply-and-demand balance. Difficulties lie in that the accurate PQ-curve expressions of wind turbines therein are unknown and nonuniform, thereby putting an obstacle to distributed optimization

Machine learning related research in transient control has drawn considerable attention with the rapid increase in data measurement from power grids. Two key components, the control algorithm and system structure, work together to determine the control performance. The design of control laws, the selection of phase measurement units, the allocation of power resources, and the scheduling of communication

Network simulation is a tool used to analyze and predict the performance of Industrial Internet of Things deployments while dealing with the complexity of real testbeds. Large network deployments with complex protocols such as transmission control protocol are subject to chaos-theory behavior, i.e., small changes in the implementation of the protocol stack or simulator behavior may result in large

The accurate time-frequency (TF) positioning of power quality (PQ) disturbances is the basis of dealing with PQ problems in power systems. To accurately detect PQ disturbances, this article proposes a Kaiser window-based S-transform (KST) that provides better time resolution at fundamental frequency to detect the amplitude information for voltage swell, sag, interrupt, flicker, and better frequency