Industrial applications involving digital twins (e.g., behavior simulation) demand highly accurate, low-latency data, making real-time data acquisition critical. To meet performance demands, devices that do not support asynchronous communication need to acquire data at high frequency. In cloud-edge collaboration schemes, edge computing nodes typically acquire the data. However, high-frequency data acquisition and processing impose considerable costs, posing significant challenges for these resource-constrained nodes. To address this problem, we propose a model called Cost-minimized Data Edge Access (CDEA) that can dynamically minimize the edge costs while satisfying long-term performance requirements. CDEA quantifies data performance by decomposing the workflow of industrial systems into basic action units. These units are used to model data acquisition, data processing, data transmission, and cloud computing. Then, a cost minimization problem is formulated based on these components. To address irregular data changes and the general lack of available statistics on system’s network status, the framework incorporates Lyapunov optimization to transform the long-term guarantee over data performance into a series of instantaneous decision problems. Finally, a heuristic algorithm identifies the optimal data acquisition strategy. To validate CDEA’s effectiveness, we implemented it in two representative digital twin scenarios: cathode plate stripping and AGV transportation. Experimental results demonstrate that CDEA can indeed reduce both edge costs and cloud resources consumption while still ensuring high data performance.

Flexible manufacturing significantly enhances production adaptability but imposes higher requirements on the design efficiency of automation control systems. Mining existing project information and transforming it into reusable function blocks is an effective approach to achieving efficient development of industrial control programs. However, the precision of manually generated function blocks depends on the individual understanding of the developer, which lacks stability and can easily cause misunderstandings in later use and maintenance. Therefore, we proposed a graph-based intelligent wrapping framework for industrial control program snippets to enhance the normalization of custom function blocks. The framework utilizes service computing and LLMs to intelligently assist in generating function blocks. The whole process includes program graph representation, semantic understanding, and automatic organization. Finally, we evaluated the proposed framework through comparative experiments in three practical projects that involved up to$\mathbf{1 0 0}$function blocks.

Recent research shows that data augmentation can mitigate data sparsity and improve the robustness of sequential recommendation. However, many augmentation strategies operate on raw sequences directly, easily disrupting the inherent semantic and temporal organization of user behaviors. To address this issue, we propose a Semantic-Aware Sequence Augmentor for recommendation that disentangles user sequences into stable (long-term) and spontaneous (short-term) latent factors, then selectively augments only the short-term component. This design preserves users’ primary semantic context while injecting controlled diversity into their short-term interests. Underlying our approach is a diffusion-based procedure that generates coherent augmented sequences without compromising semantic integrity. Extensive experiments on six real-world datasets confirm the effectiveness of SASA, showing clear improvements over conventional augmentation methods.

Image matching is an essential part of many processes in practical industrial applications. Using optimization algorithms to optimize the actual problems in industrial production can lead to more efficient use of resources. This paper presents a new algorithm called Beetle Swarm with Constrained L$\acute {e}$vy Flight (BSL) algorithm for solving problems in industrial production where image matching cannot be done quickly and accurately. This algorithm is based on the beetle antennae search algorithm. It combines the swarm intelligence algorithm with the constrained L$\acute {e}$vy flight and quickly finds the optimal solution through the long-horned beetle’s judgment of the left and right odour concentration, which reduces the blindness of L$\acute {e}$vy flight and dramatically improves the convergence speed of the algorithm. In the performance test, compared with other commonly used meta-heuristic algorithms, BSL shows stable performance with low time cost of convergence and better fitness results, which means BSL avoids the effect of random direction caused by a single beetle. In addition, this algorithm significantly improves the speed and accuracy of image matching in the application of Printed Circuit Board (PCB) defect detection. In practical application tests, the BSL algorithm was faster than the BAS algorithm, with a reduction in the fitness value. The subsequent robustness experiments further prove that the BSL algorithm has better noise immunity and is more suitable for application in actual production than Cuckoo Search (CS) and beetle antennae search (BAS).Note to Practitioners—This paper was motivated by the problem of matching images in PCB defect detection. Existing meta-heuristic approaches cannot achieve quick and accurate image matching due to the low convergence speed. This paper suggests a method that combines the beetle antennae search algorithm with the constrained L$\acute {e}$vy flight. In this paper, we improve the L$\acute {e}$vy flight mechanism to update the next position of each beetle to eliminate invalid solutions. Then, new directions are generated in combination with the odour concentration around the aspen whiskers, which reduces the blindness of L$\acute {e}$vy flight to a certain extent and, indeed, leads to the optimal solution. We also incorporate the Normalized cross-correlation (NCC) algorithm to improve the matching speed and noise immunity. The application in automated optical inspection (AOI) shows that the algorithm significantly improved the speed and accuracy when applied to production.

The secure access of numerous heterogeneous devices ensures the stability of the industrial Internet of Things. Centralized authentication can be overwhelmed by an influx of authentication requests from malicious devices. Distributed device-to-device authentication is vulnerable to tampering attack evidence. Blockchain authentication eases evidence tampering, but traditional blockchains impose high-performance requirements on devices, rendering them unsuitable for resource-limited devices. Therefore, in this article, we apply IOTA to device access authentication. To the best of authors' knowledge, this is the first application. Based on this, we propose a blockchain-enabled distributed authentication mechanism for industrial device access. The authentication involves a consensus phase based on optimized IOTA and a unique identification code validation phase. We optimized the tip selection algorithm of IOTA to make it faster and more stable. The performance experiment results demonstrate that our mechanism meets the time-consuming requirements of device access. The security experiments indicate that the authentication phases effectively intercept the attacks.

Continuous zeroing neural network (ZNN) and its discrete ZNN (DZNN) are comprehensively developed in many optimization systems. In this article, a Milne-Hamming method with DZNN classified as an implicit method is proposed and discussed upon the previous researches. Specifically, the Milne-Hamming discrete ZNN (MHDZNN) model is aimed for time-varying nonlinear optimization (TV-NO) problem with functional limitations. This Milne-Hamming (MH) method is a four-step discretized formula with fixed parameters and is introduced to discretize the ZNN model. Theoretical analyses of the MHDZNN model derive that MHDZNN possesses a larger stepsize domain $\mu \in (0,1/2)$ of absolute stability. Its convergent error is of order $O(\tau ^{5})$ and the corresponding truncation error constant is $1/40$ , which shows intimate relation to the accuracy. Compared with the existing DZNN models such as four-step explicit methods with the same $O(\tau ^{5})$ pattern, the convergent error constant of MHDZNN is smaller by a factor and maximal stability domain is greater. Finally, numerical simulations and application to redundant manipulators are provided and studied to verify the effectiveness of the proposed MHDZNN model.

In this article, we derive the predictor-corrector (PC) methods with three-order convergent precision, together with a class of specific general linear three-step (GLTS) rules provided. Afterward, a time-varying optimization (TVO) problem, which is deemed as a discrete TVO has been formulated and studied. The classical discrete zeroing neural network via Zhang et al. discretization (ZD-DZNN) is often utilized to obtain the solution. Actually, the stepsize domain of the DZNN model is a great factor for the dynamical stability. To enlarge the stepsize domain of the DZNN model, specific GLTS-type PC-DZNN models are applied to solve the TVO problem. Theoretical analyses show that better stability of the DZNN can be achieved by PC methods. Numerical simulative comparisons between the proposed PC-DZNN models and the ZD-DZNN in terms of stability are provided for further illustrations. In addition, motion planning of a PA10 manipulator and physical kinematics on UR5 formed as a TVO problem has been solved efficiently by applying the specific GLTS-type PC-DZNN models.

The rise of smart factories is driving the complete automation of manufacturing environments, making anomaly detection a more important task than ever. With highly intelligent equipment deployed on fully-automated production lines, even a slight anomaly may seriously impact the entire manufacturing process. However, most industrial data containing slight anomalous features exhibits strong correlations that are not fully exploited by existing machine learning models. In general, the anomaly detection model needs to run within programmable logic controllers (PLCs) since it is the main controller of the intelligent equipment, and PLCs have limited resources making it difficult to execute larger models effectively. To address the challenge, we propose a graph anomaly detection method in programmable logic controllers based on service computing (PCSC). The model establishes a data relationship graph through clustering and then feeds it into a neural network with a symmetric structure consisting of graph convolutional layers and LSTM units. This approach enables the analysis of correlations within industrial data and facilitates the extraction of slight abnormal features. For efficient execution of the model, we establish service computing nodes in the PLCs that support model splitting. We tested the model on several publicly available datasets and an actual dataset from an injection molding production line. The results show that the model performs well on different datasets.

To amplify the achievements on Zeroing neural network (ZNN) and widen the application of fuzzy logic system (FLS), a complex fuzzy parameter zeroing neural network (CFPZNN) model is established to address the time-variant complex Sylvester equation problem. Varying from the fixed parameters in conventional ZNN (CZNN) or time-varying parameters in ZNN (TVP-ZNN), the fuzzy parameter generated by the FLS fluctuates according with convergent error and adjusts the convergent rate adaptively. Three different activated functions (AFs) equipped with the CFP-ZNN model are analyzed and discussed. Finite convergence characteristic of the CFP-ZNN model with Signbi-power (SBP) is testified. Furthermore, various membership functions (MFs) and various fuzzy control output values are studied and compared to exhibit the performance of the CFPZNN model. Theoretical analyses and comparable simulation results among different ZNN-based neural network models in dealing with time-variant complex Sylvester equations are welly coincided.

To assist redundant manipulator to complete complex repetitive trajectory in a presented time, this article provides a solution to the kinematic assignment and presents a predefined time fuzzy zeroing neural network with event-triggered mechanism (ETM-PTFZNN). The repetitive kinematics of the redundant manipulator is originally formulated as a time-varying quadratic programming (TVQP) problem, and the ETM-PTFZNN is engaged to solve the corresponding TVQP, where the fuzzy predefined time (PT) convergence is obtained by the fuzzy system and PT activation function simultaneously. Furthermore, event-triggered mechanism is introduced to update the fuzzy parameters of the ETM-PTFZNN orderly, which greatly alleviates the calculation burden of the ETM-PTFZNN. Theoretical analyses and simulative profiles reveal that the ETM-PTFZNN model can realize PT characteristic, robustness, adaptive stability, and repetitive trajectory for the TVQP problem of kinematic planning for manipulators.

Multi-access Edge Computing (MEC) is an emerging architecture that extends cloud computing services to the edge of the network using mobile base stations. In a typical MEC paradigm edge nodes directly access heterogeneous nodes (e.g., sensors, devices, machines). However, limited to massive communication protocols of heterogeneous nodes, as well as the poor mobility, difficulty in lightweight, and low access efficiency brought by the current protocol parsing system based on the universal parser, create new challenges in the Internet of Things. We propose a dynamic protocol parsing system with optimized edge containers, called DPPS. It is suitable for MEC, allows edge nodes to dynamically access heterogeneous nodes, and supports cloud protocol parsing image management and edge node fault repair. Besides, we design a built-in container optimization algorithm based on the mutation cuckoo algorithm, which overcomes the memory resource shortage and energy consumption problems in DPPS. This algorithm demonstrates superior performance compared to other algorithms for embedded container optimization.

In the era of the Industrial Internet of Things, anomaly detection is important for real-world applications. However, most streaming data lack meaningful labels. Furthermore, some anomalies of streaming data may be concept drift, but few methods can deal with it. To address these challenges, we propose an unsupervised anomaly detection solution that can deal with streaming data, called OOA-UADS (Offline, Online, Analysis-an Unsupervised Anomaly Detection Solution for Multivariate Time Series). The solution consists of three stages: offline training, online prediction and anomaly analysis. Time convolutional networks and variational autoencoders are used to deconstruct and reconstruct the multivariate time series data to learn the normal patterns. The anomaly inversion mechanism identifies concept drift in the anomaly prediction stage by dynamically updating the classification thresholds. Intelligent anomaly analysis then provides anomaly dimensions to help engineers better analyse the anomalous behaviour. Our experiments show that OOA-UADS performs satisfactorily. On seven streaming datasets, OOA-UADS outperforms 11 baselines in terms of AUC and provides state-of-the-art F1 scores on three batch datasets.

Aiming at the problem of residual moment generated by the motion of spacecraft active loads, an improved method for measuring and calibrating it is proposed, and an on-ground residual moment measurement and calibration system based on the combination of three-dimensional force sensors and a three-axis air bearing testbed is presented for this method. The composition and working principle of the system is studied, the fixed relationship between the three-dimensional force sensors and the three-axis air bearing testbed is given, and the algorithm for measuring the residual moment of active loads is proposed. According to the algorithm, the effects of the installation error, measurement error, and measurement disturbance of the three-dimensional force sensors on the residual moment measurement accuracy are given, and the mapping relationship from different ranges and different installation errors to measurement errors of three-dimensional force sensors is studied. Theoretical and simulation analysis shows that the measurement errors of the residual moment are 0.059%, 0.064%, and 0.054%. The method is less affected by environmental interference and restrictions, with high measurement accuracy and excellent applicability.

Large and complex machines are the backbone of manufacturing and will remain key to industrialization for the foreseeable future as will leveraging essential technologies, such as the Internet of Things. However, efforts to make this machinery as smart as it could be are falling behind the curve. Data quality is often too low or too heterogeneous for useful analytics making maintenance troublesome. Additionally, many machines are still dumb with no uniform way to extract data or monitor their operation. In part, the success of future concepts like intelligent manufacturing, cyber physical systems, and industry 4.0 depends on solving these problems. Hence, this article presents FADA the groundwork for an ontology and 3-layer cloud-fog-edge architecture for large and complex machines that places data acquisition and IoT at the forefront. The ontology provides a flexible framework for standardizing data. The fog nodes acquire data directly from smart machines, while the edge nodes harvest data from dumb equipment through a recognition model. The fog nodes are flexible and multi-threaded to provide faster higher-performance computing power. To evaluate the proposed architecture and concepts, we implemented FADA in two factory-based testbeds: one with IoT-enabled equipment, the other with mostly dumb machines. The response times and influence rates recorded are promising and indicate that the system is highly adaptable to many different scenarios. We also conducted comparative experiments between FADA and a conventional data acquisition system to compare the occupied disk space, processing time, and data uploading time, which show that the FADA can save 2.9 TB of disk space per day, and reduce the server’s processing time by 184.8 ms per time over the conventional data acquisition system(CDAS), when 20000 fog nodes simultaneously access the server. The results show improvements by FADA in all metrics.

Cities are becoming smarter because of recent advances in artificial intelligence and the Internet of Things. However, heterogeneous data source in smart cities are continuously producing low-quality data, and ever-growing applications have greater real-time requirements. Therefore, this article proposes a data stream cleaning system (named DSCS) using edge intelligence to utilize the advantages of cloud servers and edge devices. The DSCS in edge nodes consists of a dynamic protocol interpreter, a structure parser, and a cleaning model activator. Meanwhile, a cloud server, which has pools of protocol and structured programs and cleaning models, supports the edge nodes to adapt massive heterogeneous data sources. To validate the proposed data cleaning system, we applied it to two scenarios: monitoring the injection molding machines, and base stations. The DSCS can have a stable processing time when the number of accessed edge devices is increased, as well as a good cleaning effect.

Environments built from the edge-based Industrial Internet of Things (IIoT) are maelstroms of information that continuously flows between heterogeneous data objects, such as sensors and devices, and edge nodes. However, the explosive growth in the number of IIoT data objects connected to edge nodes generally results in significant computation and storage requirements, which exceed those of resource-constrained edge nodes. The problem mentioned above causes a major concern related to efficient memory usage and faster communication processing in data acquisition. Therefore, in this article, we propose a multibuffers multiobjects (MBMOs) architecture to support the parallel delivery of multiple data objects to multiple variable-length buffer blocks. Furthermore, a mathematical model is established, aiming to find the optimum buffer according to the size of each communication data packet. For the aforementioned matching problem, a spatiotemporal resource allocation algorithm is designed to maximize memory usage while minimizing communication processing time. We implement our MBMO in a monitoring system in which embedded programmable logic controllers (ePLCs) serve as the edge nodes. The analyses illustrate that memory usage and time efficiency are improved greatly with the utilization of MBMO.

The merging boundaries between edge computing and deep learning are forging a new blueprint for the Internet of Things (IoT). However, the low-quality of data in many IoT platforms, especially those composed of heterogeneous devices, is hindering the development of high-quality applications for those platforms. The solution presented in this article is intelligent data collaboration, i.e., the concept of deep learning providing IoT with the ability to adaptively collaborate to accomplish a task. Here, we outline the concept of intelligent data collaboration in detail and present a mathematical model in general form. To demonstrate one possible case where intelligent data collaboration would be useful, we prepared an implementation called adaptive data cleaning (ADC), designed to filter noisy data out of temperature readings in an IoT base station network. ADC primarily consists of a denoising autoencoder LSTM for predictions and a four-level data processing mechanism to perform the filtering. Comparisons between ADC and a maximum slop method show ADC with the lowest false error and the best filtering rates.

Spectrum reutilization is a potential aspect in cognitive radio (CR) and has been employed recently in the shared spectrum sub-6 GHz. Such application has been supported by technologies that can manage the interference at other networks while providing acceptable services for the CR network itself. The multiple input multiple output (MIMO) technology brings benefits to CR with regard to the interference management making it a promising technology for Industry 4.0. In this paper, we propose a MIMO system for industrial CR networks. The proposed MIMO improves not only the spectral efficiency performance, but also the latency. Our model addresses industrial augmented reality application which requires high data rate and low latency. Specifically, the proposed scheme employs efficient precoding and deep learning for solving non-convex power optimization problem. The numerical results demonstrate outstanding performance in sake of our proposal.

Edge computing has enabled extensive reasoning capabilities at the edge of the network. Edge nodes have direct access to edge sources (e.g., sensors, devices) to read and process relevant data, while each sensor/device manufacturer uses their bespoke protocols in the Internet of Things (IoT) environment, hence, addressing compatibility problems to ensure seamless access has become a cumbersome task. A conventional solution is to save all sensor/device protocols in an edge node, however, which brings higher redundancy and hardware expenditure. Hence, we propose a dynamic edge access system (DEA) built on a collaborative IoT architecture. It solves the compatibility issues with low redundancy by customizing access programs. Besides, embedded programmable logic controllers are designed as edge nodes and have been applied in around 78 000 base stations. After implementing DEA, the analysis illustrates that the average improvement of every station's protocol utilization is 96.3% and 66.6%, while the memory footprint is decreased to 4.5% and 33.3% compared with the conventional solution and global dynamic reconfiguration method.

General programmable logic controllers (PLCs) are difficult to adapt to changeable applications, especially for those with special motion control; this has seriously affected the development efficiency. This paper presents the concept of the embedded PLC (ePLC), whose hardware structure can be customized according to actual requirements. We proposed a three-layer architecture, and its customizable application layer could be compiled in real time. Correspondingly, the PLC program was divided into an engine program, control program, and customizing program. The description and compilation method of the customizing program was provided. We presented a customized winding machine language based on the proposed ePLC software structure. This was implemented in an automatic winding machine, which was easier to use compared with some widely used languages (e.g., G-Code).