Industrial Internet upgrades the traditional industrial manufacturing to digitization, networking and intellectualization era, which calls for brand-new technology supports. As a promising solution, the emergence Digital Twin (DT) offers enhanced digital mapping capability with strong feasibility, security, economic and intelligence, which fits well with the concept of Industrial Internet. In this paper, we focus on establishing a new reference architecture of DT to support the development of Industrial Internet. It is composed of three interdependent layers (i.e., physical layer, DT layer and DT networks layer) and four critical attributes (i.e., privacy, security, awareness and real-time). We illustrate our perspectives for the functionality and relationship of the three layers, and features and feasible solutions of the four attributes. With those efforts, the proposed DT architecture can provide both smart manufacturing and networked services for Industrial Internet era. Moreover, we also illustrate the relevant and open challenges. Finally, the conclusion and future perspective are pointed out.

This paper gives a definition of the Industrial Internet and expounds on the academic connotation of the future Industrial Internet. From this foundation, we outline the development and current status of the Industrial Internet in China and globally. Moreover, we detail the avant-garde paradigms encompassed within the National Natural Science Foundation of China (NSFC)\'s \"Future Industrial Internet Fundamental Theory and Key Technologies\" research plan and its corresponding management strategies. This research initiative endeavors to enhance interdisciplinary collaborations, aiming for a synergistic alignment of industry, academia, research, and practical implementations. The primary focus of the research plan is on the pivotal scientific challenges inherent to the future industrial internet. It is poised to traverse the \"first mile\", encompassing foundational research and pioneering innovations specific to the industrial internet, and seamlessly bridges to the \"last mile\", ensuring the effective commercialization of scientific and technological breakthroughs into tangible industrial market applications. The anticipated contributions from this initiative are projected to solidify both the theoretical and practical scaffolding essential for the cultivation of a globally competitive industrial internet infrastructure in China.

With the advent of the Internet of Everything (IoE), the concept of fully interconnected systems has become a reality, and the need for seamless communication and interoperability among different industrial systems has become more pressing than ever before. To address the challenges posed by massive data traffic, we demonstrate the potentials of semantic information processing in industrial manufacturing processes and then propose a brief framework of semantic processing and communication system for industrial network. In particular, the scheme is featured with task-orientation and collaborative processing. To illustrate its applicability, we provide examples of time series and images, as typical industrial data sources, for practical tasks, such as lifecycle estimation and surface defect detection. Simulation results show that semantic information processing achieves a more efficient way of information processing and exchanging, compared to conventional methods, which is crucial for handling the demands of future interconnected industrial networks.

The traditional Transformer model primarily employs a self-attention mechanism to capture global feature relationships, potentially overlooking local relationships within sequences and thus affecting the modeling capability of local features. For Support Vector Machine (SVM), it often requires the joint use of feature selection algorithms or model optimization methods to achieve maximum classification accuracy. Addressing the issues in both models, this paper introduces a novel network framework, CTSF, specifically designed for Industrial Internet intrusion detection. CTSF effectively addresses the limitations of traditional Transformers in extracting local features while compensating for the weaknesses of SVM. The framework comprises a pre-training component and a decision-making component. The pre-training section consists of both CNN and an enhanced Transformer, designed to capture both local and global features from input data while reducing data feature dimensions. The improved Transformer simultaneously decreases certain training parameters within CTSF, making it more suitable for the Industrial Internet environment. The classification section is composed of SVM, which receives initial classification data from the pre-training phase and determines the optimal decision boundary. The proposed framework is evaluated on an imbalanced subset of the X-IIOTID dataset, which represent Industrial Internet data. Experimental results demonstrate that with SVM using both \"linear\" and \"rbf\" kernel functions, CTSF achieves an overall accuracy of 0.98875 and effectively discriminates minor classes, showcasing the superiority of this framework.

UNASSIGNED: The industrial Internet represents a fundamental infrastructure for future digitalization, networking, intelligent change, and innovation in the manufacturing industry. In this study, an index system was constructed to evaluate the maturity level of the industrial Internet in the building materials industry.
UNASSIGNED: Based on factor analysis and the entropy weight method, we constructed a method of evaluating the maturity level of industrial Internet for building materials companies. Specifically, an industrial Internet maturity evaluation index system for building materials companies was established through factor analysis, and the weights of various evaluation indexes were assigned using the entropy method. In our study, 59 representative building materials companies were selected as research samples, and empirical research was conducted. Thereafter, one company was selected to verify the evaluation model. Finally, the comprehensive scores and rankings of the 59 building materials companies and their individual scores were calculated.
UNASSIGNED: This study identified seven key indicators for assessing industrial Internet maturity in the building materials industry: data analysis and usage, information network facilities, intelligence level of the system, smart device networking, comprehensive integration, safety management digitization rate, and R & D digitization rate. The study indicated that the majority of large-scale building materials companies were at a 2-star level of industrial Internet maturity, indicating an overall primary stage. The study revealed that information network infrastructure had a relatively high level, but comprehensive integration and data analysis capacity were comparatively weak. Moreover, the R&D digitalization rate and the safety management digitalization rate showed consistent development levels.
UNASSIGNED: This study introduces the first maturity model for the industrial internet in the building materials industry, guiding government decision-making and providing self-assessment direction for companies. It aims to effectively address the industry\'s challenges of high energy consumption, emissions, labor shortages, and low efficiency.

In recent years, cloud-native technology has become popular among Internet companies. Microservice architecture solves the complexity problem for multiple service methods by decomposing a single application so that each service can be independently developed, independently deployed, and independently expanded. At the same time, domestic industrial Internet construction is still in its infancy, and small and medium-sized enterprises still face many problems in the process of digital transformation, such as difficult resource integration, complex control equipment workflow, slow development and deployment process, and shortage of operation and maintenance personnel. The existing traditional workflow architecture is mainly aimed at the cloud scenario, which consumes a lot of resources and cannot be used in resource-limited scenarios at the edge. Moreover, traditional workflow is not efficient enough to transfer data and often needs to rely on various storage mechanisms. In this article, a lightweight and efficient workflow architecture is proposed to optimize the defects of these traditional workflows by combining cloud-edge scene. By orchestrating a lightweight workflow engine with a Kubernetes Operator, the architecture can significantly reduce workflow execution time and unify data flow between cloud microservices and edge devices.

With the large distributed, autonomous, diverse, and dynamic information sources generated in the Industrial Internet area, the information model becomes the critical technology for heterogeneous data interoperability. By establishing unified architecture, mutually agreed communication protocols and standardizing syntax and semantics, the potential of complex data can be released. However, most of the existing information models are isolated in the professional fields, and the interoperability and scope of standards are very limited. In this article, we design a uniform information model for the Industrial Internet, and present a general modeling method which aims to build a standardized organizational framework of information. Specifically, the Industrial Internet information model is first defined, where the seven key elements and value evaluation are devised for information extraction. Then, an optimization approach combining entropy and semantic distance theories is proposed that determines the information organization granularity. Next, as the cross-layer interaction of complex information is very tricky in a tree structure and its modeling cost is extremely high in a mesh topology, the underground root structure is invented for model representation. Finally, the modeling methodology is applied to the ordinary and precision machine tools demonstrating 18.75% and 18.18% modeling cost reduction, respectively, and these two information models are further implemented in a digital machining workshop to verify the effectiveness of the proposed modeling method.

Blockchain has become one of the key techniques for the security of the industrial internet. However, the blockchain is vulnerable to FAW (Fork after Withholding) attacks. To protect the industrial internet from FAW attacks, this paper proposes a novel FAW attack protection algorithm (FAWPA) based on the behavior of blockchain miners. Firstly, FAWPA performs miner data preprocessing based on the behavior of the miners. Then, FAWPA proposes a behavioral reward and punishment mechanism and a credit scoring model to obtain cumulative credit value with the processed data. Moreover, we propose a miner\'s credit classification mechanism based on fuzzy C-means (FCM), which combines the improved Aquila optimizer (AO) with strong solving ability. That is, FAWPA combines the miner\'s accumulated credit value and multiple attack features as the basis for classification, and optimizes cluster center selection by simulating Aquila\'s predation behavior. It can improve the solution update mechanism in different optimization stages. FAWPA can realize the rapid classification of miners\' credit levels by improving the speed of identifying malicious miners. To evaluate the protective effect of the target mining pool, FAWPA finally establishes a mining pool and miner revenue model under FAW attack. The simulation results show that FAWPA can thoroughly and efficiently detect malicious miners in the target mining pool. FAWPA also improves the recall rate and precision rate of malicious miner detection, and it improves the cumulative revenue of the target mining pool. The proposed algorithm performs better than ND, RSCM, AWRS, and ICRDS.

Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows. Traditional routing algorithms are not applicable for this novel type of network, as they cause unnecessary congestion and latency. Therefore, this study examined the classification of TSN flows in smart factories, converted the routing problem into two graphical problems, and proposed two heuristic optimization algorithms, namely GATTRP and AACO, to find the optimal solution. The experiments showed that the algorithms proposed in this paper could provide a more reasonable routing arrangement for various TSN flows with different time sensitivities. The algorithms could effectively reduce the overall delay by up to 74% and 41%, respectively, with promising operating performances.

In the Industrial Internet, computing- and power-limited mobile devices (MDs) in the production process can hardly support the computation-intensive or time-sensitive applications. As a new computing paradigm, mobile edge computing (MEC) can almost meet the requirements of latency and calculation by handling tasks approximately close to MDs. However, the limited battery capacity of MDs causes unreliable task offloading in MEC, which will increase the system overhead and reduce the economic efficiency of manufacturing in actual production. To make the offloading scheme adaptive to that uncertain mobile environment, this paper considers the reliability of MDs, which is defined as residual energy after completing a computation task. In more detail, we first investigate the task offloading in MEC and also consider reliability as an important criterion. To optimize the system overhead caused by task offloading, we then construct the mathematical models for two different computing modes, namely, local computing and remote computing, and formulate task offloading as a mixed integer non-linear programming (MINLP) problem. To effectively solve the optimization problem, we further propose a heuristic algorithm based on greedy policy (HAGP). The algorithm achieves the optimal CPU cycle frequency for local computing and the optimal transmission power for remote computing by alternating optimization (AP) methods. It then makes the optimal offloading decision for each MD with a minimal system overhead in both of these two modes by the greedy policy under the limited wireless channels constraint. Finally, multiple experiments are simulated to verify the advantages of HAGP, and the results strongly confirm that the considered task offloading reliability of MDs can reduce the system overhead and further save energy consumption to prolong the life of the battery and support more computation tasks.