Network automation has originated in the early 21st century by the International Business Machines Corporation (IBM), which was initialized conceptually, including automated configuration, optimization, healing, and protection of network deployment. In the framework of 5G and upcoming 6G, softwarization and virtualization, as well as the conceived pervasive artificial intelligence (AI), have been activating

Deep learning technology has become one of the core driving forces to promote the in-depth development of industrial automation. In [A1], Wang et al. interpreted the decision process of the convolutional neural network (CNN) by constructing a percolation model from a statistical physics perspective. In this perspective, the decision-making basis of CNN is difficult to understand, because CNN is usually

Network automation has been bred by the deployment of 5G based Industrial Internet-of-Things (IIoT) in Industry 4.0, and further approaching pervasive AI and human-robot-interaction/-collaboration toward 6G based Industry 5.0. Hitherto, to the best of the authors knowledge, research efforts are still required to provide a comprehensive review of the state-of-the-art network automation technologies

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

Industry 5.0 aims at maximizing the collaboration between humans and machines. Machines are capable of automating repetitive jobs, while humans handle creative tasks. As a critical component of Industrial Internet of Things (IIoT) systems for service delivery, network data stream analytics often encounter concept drift issues due to dynamic IIoT environments, causing performance degradation and automation

The widespread adoption of Industrial Internet of Things (IIoT)-based applications has driven the emergence and development of cloud-related computing paradigms with the ability to seamlessly leverage cloud resources. Heterogeneous resources, mobility factors in IoT, and dynamic behavior make it challenging for the corresponding virtual machine (VM) scheduling problem to address the processing effectiveness

As an essential approach in many Internet of Things (IoT) applications, multiview learning synthesizes multiple features to achieve more comprehensive descriptions of data items. Most of the previous studies on multiview learning have been dedicated to increasing the prediction accuracy, while ignoring the reliability of the decision. This would limit their deployment in high-risk IoT and industrial

Current cyberspace is confronted with unprecedented security risks, whereas traditional passive protection techniques are ill-equipped for attacks or defects with unknown features. Dynamic heterogeneous redundancy (DHR), a built-in active defense approach, deploys uncertain, random, dynamic systems to change the asymmetry of attack and defense, where arbitration is one of the key mechanisms. In this

Monitoring and forecasting of sintering temperature (ST) is vital for safe, stable, and efficient operation of rotary kiln production process. Due to the complex coupling and time-varying characteristics of process data collected by the distributed control system, its long-range prediction remains a challenge. In this article, we propose a multivariate time series forecasting model based on dynamic

In this article, we propose a trustworthy privacy-preserving federated learning (FL)-based deep learning (DL) service framework for Industrial Internet of Things-enabled systems. FL mitigates the privacy issues of the traditional collaborative learning model by aggregating multiple locally trained models without sharing any datasets among the participants. Nevertheless, the FL-based DL (FDL) model

Deep reinforcement learning (DRL) based recommender systems are suitable for user cold-start problems as they can capture user preferences progressively. However, most existing DRL-based recommender systems are suboptimal, since they use the same policy to suit the dynamics of different users. We reformulate recommendation as a multitask Markov Decision Process, where each task represents a set of

Industrial Internet of Things (IIoT) is revolutionizing plenty of industrial applications by utilizing large-scale smart devices in manufacturing and industrial processes. However, IIoT is facing the disclosure of identity privacy. The identity information is precious and critical, thereby inspiring a line of follow-up privacy-preserving studies, i.e., anonymous credential protocols, or privacy-preserving

Today's Industrial Internet of Things (IIoT) have achieved excellent manufacturing efficiency and automation results by leveraging machine learning (ML) and deep learning (DL). However, trustworthiness of ML/DL brings significant challenges to IIoT. This article proposes an evolving deep multiple kernel learning network through genetic algorithm (KNGA). Our KNGA method uses genetic algorithm (GA) to

With the increasingly humanized and intelligent operation of Industrial Internet of Things (IIoT) systems in Industry 5.0, delay-sensitive and compute-intensive (DSCI) devices have proliferated, and their demand for low latency and low power consumption has become more and more eager. In order to extend the battery life and improve the quality of user experience, we can offload DSCI-type workloads

Time-sensitive networking (TSN), an emerging network technology, requires high-performance scheduling mechanisms to deliver deterministic service in Industry 5.0. Cyclic queuing and forwarding (CQF) is launched to simplify the configuration complexity of the early stage mechanism time-aware shaper in TSN flow scheduling. Previous CQF studies adopt an inflexible incremental flow scheduling scheme, which

The adoption of voice user interface (VUI) will promote network automation with enhanced efficiency with reduced simplicity and operating expense in Industry 5.0. Given the noisy environments, speech denoising is indispensable for the VUI in Internet of Things (IoT) or Industrial IoT (IIoT). Despite Transformer's recent success in speech denoising, the adopted full self-attention suffers from quadratic

As a significant ingredient regarding health status, data-driven state of health (SOH) estimation has become dominant for lithium-ion batteries. To handle data discrepancy across batteries, current SOH estimation models engage in transfer learning (TL), which reserves a priori knowledge gained through reusing partial structures of the offline trained model. However, multiple degradation patterns of

Process monitoring, a typical application of industrial Internet of Things (IIOT), is crucial to ensure the reliable operation of the industrial system. In practice, due to the harsh environment and unreliable sensors and actuators, it is often difficult for IIoT to collect enough tagged and highly reliable data, which further degrades the process monitoring performance and makes the monitoring results

5G is becoming the foundation for the Industrial Internet of Things (IIoT) enabling more effective low-latency integration of artificial intelligence and cloud computing in a framework of a smart and intelligent IIoT ecosystems enhancing the entire industrial procedure. However, it also increases the functional complexities of the underlying control system and introduces new powerful attack vectors

Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent manufacturing systems. Deep learning-based methods have been recently developed for fault diagnosis due to their advantages in feature representation. However, most of these methods fail to learn relations between samples and thus perform poorly without sufficient labeled data. In this article, we propose

Monitoring security and quality of service is essential, due to the rapid growth of the number of nodes in wireless networks. In healthcare/industrial environments, especially in wireless body area networks (WBANs), this is even more important. Because the delays and errors can directly affect patients'/scientists' health. To increase the Monitoring Quality of Services (MQoS) in WBANs, a secure medium

Synchrotron X-ray microdiffraction ( μ XRD) services are conducted for industrial minerals to identify their crystal impurities in terms of crystallinity and potential impurities. μ XRD services generate huge loads of images that have to be screened before further processing and storage. However, there are insufficient effective labeled samples to train a screening model since service consumers are

In recent years, the Internet of Things (IoT) and enterprise management systems (EMS) have been rapidly growing and applied in advanced Industries. It provides better big data analytics and the most promising computing platforms. Moreover, IoT is transforming into the augmented intelligence of things (AIoT), developing a human-oriented paradigm for enterprises with AI. Still, smart enterprises and

As part of Big Data trends, the ubiquitous use of the Internet of Things (IoT) in the industrial environment has generated a significant amount of network traffic. In this type of IoT industrial network where there is a large equipment heterogeneity, security is a fundamental issue; thus, it is very important to detect likely intrusion behaviors. Furthermore, since the proportion of labeled data records

The electric vehicle (EV) charging ecosystem, being a distinguishable paradigm of IIoT infrastructure, consists of distributed and complex hybrid systems that demand adaptive data-driven cyber-defense mechanisms to tackle the ever-growing attack vectors of cyber-physical systems. We propose an adaptive differential privacy-based federated learning framework for building a collaborative network intrusion

Trustworthy and real-time video surveillance aims to analyze the live camera streams in a privacy-preserving manner for the decision-making of various advanced services, such as pedestrian reidentification and traffic monitoring. In recent years, edge computing has been identified as a promising technology for trustworthy and real-time video surveillance because it keeps confidential video data locally

Recommender systems are technology-driven marketing solutions for businesses that analyze user behavior data. However, collaborative data sharing between enterprises is often prohibited by privacy protection regulations, leading to insufficient data for graph neural networks (GNNs) training. Fortunately, federated learning (FL), a collaborative training framework without exposing source data, can be

Finding influential nodes reshuffles the very notion of linear paths in business processes and replaces it with networks of business value within a smart enterprise system. There are many existing algorithms for identifying influential nodes with certain limitations for applying in large-scale networks. In this article, we propose a community structure with integrated features ranking (CIFR) algorithm

Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professionals like doctors or physicians analyze patients' health status to cure different diseases. However, collecting such information from an insecure channel can be threatening due to the potential

Artificial intelligence-driven automation has gradually become the technical trend of the new automation era. At present, many artificial intelligence technologies have been applied to improve the intelligence level in the field of automation. Among them, convolutional neural network (CNN) technology is one of the most representative, which is used in the detection of defective products in industrial

Intelligent connected vehicle is rapidly growing with the 5-G technology; the diversity of functional interfaces has significantly expanded the avenues of attack, making automotive controller area network (CAN) more vulnerable to cyberthreats. Automotive CAN network attacks are a direct threat to traffic safety, and in this study, we explore the use of intrusion detection techniques for mitigating

Crack is one of the critical factors that degrade the performance of machinery manufacturing equipment. Recently, physics-informed neural networks (PINNs) have received attention due to their strong potential in solving physical problems. For fracture problems, PINNs have been used to predict crack paths by minimizing the variational energy of discrete domains where refined meshes are necessary. To

Fixed-time and prescribed-time consensus control can bring an explicit estimate of the settling time without dependence on initial conditions, which is important in providing control engineers a priori system information. This article aims at presenting a survey of recent trends and methodologies of fixed-time and prescribed-time consensus control in multiagent systems. First, some typical fixed-time

Progress of quantum computing technology seriously threaten the industrial information security based on traditional public-key cryptosystem. Thus, the cryptosystem with anti-quantum attack characteristics is gradually becoming a significant research in the security field. In this article, a flexible and high-performance secure coprocessor is designed for security in industrial processes, which can

The health monitoring of traction motors is crucial for the prognostics and health management of high-speed trains. The temperature signal is an outstanding health indicator. Due to the representing of the traction motor's health conditions and low cost, accurate prediction for the motor temperature is conducive to early detection of abnormalities. However, the traditional prediction models are trained

Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices, including cell phones and other sensing devices in location-based social network. It can help traveling enterprises intelligently predict users' interests and preferences, provide them with scientific tourism paths, and increase the enterprises income. Thus, successive point-of-interest

The Augmented Intelligence of Things enables many edge or end devices in the Internet of Things (IoT) to perform machine reasoning to make decisions, thus become more intelligent. For healthcare enterprises, huge physical data generated by smart devices facilitate to iterate their products. However, traditional data sharing models based on cloud outsourcing meet many security challenges, such as data

Service composition enables the flexible and agile collaboration of multiple services to complete personalized manufacturing tasks in cloud manufacturing. Compared with traditional manufacturing mode and cloud computing, trust problems become more serious and crucial in cloud manufacturing because of the nontransparency and short-term cooperation mode. A trust evaluation method for service composition

The intelligent detection process for industrial anomalies employs artificial intelligence methods to classify images that deviate from a normal appearance. Traditional convolutional neural network (CNN)-based anomaly detection algorithms mainly use the network to restructure abnormal areas and detect anomalies by calculating the errors between the original image and reconstructed image. However, the

The speckle noise is a granular disturbance that often brings negative side effects on the detection and recognition of targets of interest in industrial imaging sensors. From the statistical point of view, this type of noise can be modeled as a multiplicative formula. The nonlinear multiplicative property makes despeckling more intractable with respect to noise reduction and details preservation.

Cyber-physical systems (CPSs) have attracted increasing attention in recent years due to their promise for substantial and long-term benefits to society, economy, environment, and citizens. In addition, the rapid advances in computing, communication, and storage technologies have resulted in a revolution in the information communication technology domain and domination in the industry context. The

With the rapid progress of the industrial Internet of Things (IIoT), reducing data uncertainty has become a critical issue in predicting the development trends of systems and formulating future maintenance strategies. This article proposes an end-to-end, deep hybrid network-based, short-term, multivariate time-series prediction framework for industrial processes. First, the maximal information coefficient

With the unprecedented development of deep learning, autonomous vehicles (AVs) have achieved tremendous progress nowadays. However, AV supported by DNN models is vulnerable to data poisoning attacks, hindering the large-scale application of autonomous driving. For example, by injecting carefully designed poisons into the training dataset of the DNN model in the traffic sign recognition system, the

The brain-computer interface (BCI) is a cutting-edge technology that has the potential to change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used extensively in many BCI applications to assist disabled people, control devices or environments, and even augment human capabilities. However, the limited performance of brain signal decoding is restricting the broad growth of

Augmented intelligence is an innovative amplification of artificial intelligence that allows human experts to take over the autonomous decision of machines. It also facilitates human-intelligence-based decisions on the network edge using low-cost and small-sized devices. Augmented intelligence and the Internet of Things collectively create augmented intelligence of things. It logically and effortlessly

In modern industries, data-driven fault detection and classification (FDC) systems can efficiently maintain industrial security and stability, while the security of the data-driven FDC system itself is rarely or even never considered. The security problem named adversarial vulnerability is the intrinsic of data-driven machine learning models, which will give incorrect predictions under the maliciously

Nowadays, much research leverages the clustering to mine commercial patterns from data in enterprise systems. However, previous methods cannot fully consider local structures and global topology of data, which may cause the degradation of clustering performance. To address the challenges, a deep multimodal adversarial cycle-consistent network (DMACCN) is proposed to mine intrinsic patterns of data

With the increasing data scale in the Industrial Internet of Things, edge computing coordinated with machine learning is regarded as an effective way to raise the novel latency-sensitive services. To ensure the data privacy for frequent service access, federated learning (FL), as a privacy-preserving distributed framework, is integrated into edge computing, enabling user data invisible to the training

The unalterable nature of fingerprint biometrics leads to permanent identity loss of a user if an original fingerprint template is compromised. Moreover, it is evident in the literature that reconstructing a fingerprint image from an original fingerprint template is a feasible task. In order to protect the fingerprint template, we propose a noninvertible and alignment-free fingerprint template protection

Sizeable lithium-ion battery (LIB) sources in the transportation and power sectors provide a promising approach to alleviate the increasing volatility in energy systems. To dispatch LIBs durably and safely, operators need to estimate the battery power characteristics, which are commonly derived from external states of the battery obtained by empirical models. However, the internal states that play

The relationship between computing and healthcare has a long history, but adoption of telemedicine is gradual due to political resistance, lack of infrastructure development frameworks, and lack of resources. One of the most rapid technological advancements will be the Internet of Medical Things (IoMT), which is predicted to bring about the greatest technological delivery ever. Edge computing in conjunction

Physics-informed learning for industrial Internet is essential especially to safety issues. Consequently, various methods have been developed to conduct Industrial Internet of Things (IIoT) intrusion detection. However, the conventional methods usually require the help of auxiliary equipment (e.g., spectrum analyzers, log-periodic antennas), which proves to be unsuitable for general IIoT systems due

Automatic recommendation has become an increasingly relevant problem for industries, which allows users to discover items that match their tastes and enables the system to target items at the right users. Graph neural networks have attracted many researchers' attention and have become a useful tool for recommendation. However, these models face two major challenges, which are heterogeneous information

Several symmetric and asymmetric encryption based authentication protocols have been developed for the wireless local area networks (WLANs). However, recent findings reveal that these protocols are either vulnerable to numerous attacks or computationally expensive. Considering the demerits of these protocols and the necessity to provide enhanced security, a lightweight extensible authentication protocol

The real-time electrical equipment management, such as renewable energy, controllable loads, and storage units, plays a key role in low-carbon operation of smart industrial park. Digital twin (DT), which explores cloud-edge-device collaboration and artificial intelligence to establish accurate digital representation of physical equipment, is a cutting-edge technology to realize intelligent optimization

In order to help robots understand and perceive an object's properties during noncontact robot-object interaction, this article proposes a deeply supervised subspace learning method. In contrast to previous work, it takes the advantages of low noise and fast response of noncontact sensors and extracts novel contactless feature information to retrieve cross-modal information, so as to estimate and infer

Building energy prediction plays critical roles in the study of green building and smart city. The most challenging issue is to predict energy demand profiles over multiple time steps, which may have inconsistent timescales. Due to complex temporal dependence, existing prediction approaches cannot satisfy certain requirements in multistep (MS) or multitimescale (MTS) applications. In this article,