Application Of Industrial Internet Research Articles

A robust and effective 3-factor authentication protocol for smart factory in IIoT

Smart factory, as an intelligent application of industrial internet of things (IIoT), significantly enhances the efficiency of industrial processes while also reducing resource waste. In a system where security is crucial, the user authentication mechanism is essentially designed to prevent a range of security issues, including production failures caused by illegal intrusions from hackers. To verify the user’s identity and ensure data be accessed with a secure session key, a large number of authentication protocols have been provided for IIoT. However, existing newly alternatives show deficiency either in terms of superior performance or robust security. To get a better balance of security and efficiency, this paper designs a robust and effective 3-factor user authentication protocol. Then this paper shows the detailed security analyses and automated verification by the Proverif tool, where the ProVerif tool used in this paper can detect whether the eCK adversary with stronger attack ability can break the protocol, while the general ProVerif can only detect threat of the Dolev–Yao adversary with weaker attack ability to the protocol security. Subsequently, this paper presents performance comparison which indicates that the proposed protocol can be superior to those newly alternatives. Especially, the comparison results show that the computation cost consumed in the proposed protocol can be reduced by 62.2% than the average cost of all six compared alternatives. Lastly, the evaluation results on the energy consumption and the network delay indicate the practicability of proposed protocol for smart factory.

IoT Real-Time Production Monitoring and Automated Process Transformation in Smart Manufacturing

Conventional automobile manufacturing plants involve intricate assembly, testing, and debugging processes heavily reliant on manual operations. This study aims to explore the application of industrial internet of things (IIoT) and deep learning algorithms to achieve process automation in manufacturing. Firstly, utilizing IIoT technology, OPC UA, and point cloud fitting techniques, a comprehensive modeling of most equipment and materials within the factory is conducted, constructing a digital twin (DT) model as a virtual representation of actual equipment. Subsequently, the study innovatively introduces the deep Q network algorithm, facilitating the automatic transition of the production process and improving production efficiency. Through comparison with ten baseline models, the proposed model demonstrates an improvement in production efficiency of at least four percentage points compared to other models. Experimental validation confirms the effectiveness of the proposed model in the smart factory for electric vehicle manufacturing.

Routing and scheduling co-design for holistic software-defined deterministic network

As a result of the growing complexity of industrial Internet applications, traditional hardware-based network designs are encountering challenges in terms of programmability and dynamic adaptability as they struggle to meet the real-time, high-reliability transmission requirements for the vast quantities of data generated in industrial environments. This paper proposes a holistic software-defined deterministic network (HSDDN) design solution. This solution uses a centralized controller to implement a comprehensive software definition, ranging from the network layer down to the physical layer. Within the wireless access domain, we decouple the standard radio-frequency modules from baseband processing to realize a software-defined physical layer, which then allows us to adjust the data transmission cycles and tag the trigger rates to meet demand for low-power, high-concurrency transmission. Within the wired network domain, we integrate software-defined networking with time-sensitive networking and propose a coordinated design strategy to address routing and the deterministic scheduling problem. We define a set of constraints to ensure collaborative transmission of the periodic and aperiodic data flows. To guarantee load balancing across all paths and timeslots, we introduce the Jain’s fairness index as the optimization objective and then construct a nondeterministic polynomial-time (NP)-hard joint optimization problem. Furthermore, an algorithm called Tabu search for routing and scheduling with dual-stages (TSRS-DS) is proposed. Simulation experiments demonstrate the effectiveness of the proposed HSDDN architecture.

Design of compound data acquisition gateway based on 5G network

With the wide application of industrial Internet of Things, the increasing amount of data and the complexity of data types, higher requirements are put forward for the performance of data acquisition gateway. In order to reduce the data acquisition time of the gateway and improve the data retrieval coverage of the gateway, a novel design method of composite data acquisition gateway based on 5G network is proposed. Based on the analysis of related technologies, the functional requirements of the composite data acquisition gateway are summarized, and the overall design of the gateway is completed. On this basis, the gateway hardware environment is constructed by designing the main control module, 5G module and FPGA program, and then the software program is designed by designing the data acquisition driver, 5G module driver, embedded software and protocol conversion process. The experimental results show that the data retrieval coverage of the gateway designed by this method is always above 92%, which is 6% higher than that of method 1. This shows that the method significantly improves the coverage of data search, speeds up the efficiency of data collection, and improves the performance of the data collection gateway, which proves the effectiveness and feasibility of the method and is conducive to promoting the intelligent development of the data collection gateway technology.

Switchable operating modes enable low power consumption and improved gas sensing efficiency in MoS2/BP heterojunction

Low power consumption and reliable response are two main requirements for gas sensors to be suitable for emerging Internet of Things and Industrial Internet applications. However, state-of-the-art inorganic chemiresistor-type gas sensors have difficulty overcoming the limitations of high-power consumption. Integrating complementary photovoltaic effect and gas sensing functions in a single heterojunction is an alternative approach. Herein, we present diode-type gas sensors based on two-dimensional heterojunction, which can switch the operating modes by adjusting the bias voltage or illumination condition. At forward bias, the sensor has a high response of 310 and good selectivity, while in self-powered mode, it exhibits an open circuit voltage of 0.36 V, a fast recovery rate (27.3 s), and low power consumption (20 nW). Furthermore, the gas sensing mechanism at room temperature was confirmed by energy potential, photoluminescence, Raman spectra measurements, and the DFT calculations before and after NO2 exposure, indicating that the electron transfer between the heterojunction and NO2 molecules was the dominant factor. The proposed gas sensor with the switchable operating modes may open new avenues for the development of advanced energy-efficient, high-efficiency gas sensing devices, and concepts.

Toward Personalized Federated Learning Via Group Collaboration in IIoT

Despite the rapid growth of successful examples of Federated Learning (FL), it faces the heterogeneity of data, models, and devices in emerging applications of Industrial Internet of Things (IIoT). Existing efforts mainly focus on training multiple personalized models by adopting a global, cluster, or pairwise fashion. However, the global collaboration doesn't work well in case of the non-IID data distribution, cluster collaboration is often inefficient due to the single cluster pattern and high computational cost, and pairwise collaboration incurs the limitations of collaboration scope and communication efficiency. To address the problems, we propose a novel Personalized FL (PFL) framework with the game-theoretic insights, called group collaboration, to overcome the shortcomings of status quo. Specifically, we first formulate the group collaboration in PFL as a multi-leader multi-follower Stackelberg game, and then develop an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -better response to efficiently characterize its unique equilibrium through cautiously proposing a potential function. Since the existing equilibrium may not be optimal, we further design a Robin Hood mechanism by using the idea of transferable utility to improve the performance of the training model. Meanwhile, we also prove that the new mechanism is sustainable and can converge to a stable state with an upper bound of the training loss. Last, extensive experiments on a simulated dataset and four real-world datasets demonstrate the superiority of our proposed approach compared to the state-of-the-art.

Application of industrial Internet of things technology in fault diagnosis of food machinery equipment based on neural network

Application of industrial Internet of things technology in fault diagnosis of food machinery equipment based on neural network

Path and Mechanism of Industrial Internet Industry Promoting the Transformation and Upgrading of Small and Medium-sized Enterprises with Artificial Intelligence

With the development of intelligent sensing and communication, industrial Internet of Things (IoT) networks have been widely applied in many application scenarios. As a typical application of industrial Internet of Things (IoT) networks, industrial Internet industry has attracted much attention from researchers. Specifically, in the transformation and upgrading of small enterprises in the Internet industry, their paths and mechanisms are very important. The correct path and mechanism of transformation and upgrading can promote national economic growth, strengthen market competition, enhance market vitality, and optimize economic structure. Small and medium-sized enterprises have always been the backbone of my country’s economic development, especially in terms of promoting employment, promoting technological innovation, increasing national taxes, and improving people’s livelihood. In this paper, when discussing the relationship between knowledge sharing and business performance of SMEs, the organization name is introduced as an intermediate variable. By combining theoretical research and empirical research, a theoretical model is established, research hypotheses are proposed, and the previous research questionnaire is used, by exploiting the intelligent sensing and communication. As a reference, a research questionnaire suitable for the reality and needs of this research was designed. On this basis, this article first has a deep understanding of the research status of comprehensive budget management at home and abroad and analyzes the innovations and shortcomings of this article in the analysis process, understands the mobile Internet, integrates budget management, and integrates information for budget management. It prove the feasibility of helping small and medium-sized enterprises to transform in the mobile Internet environment and analyzes the feasibility of implementation to help small and medium-sized enterprises; it also shows that the industrial Internet industry is helpful to the transformation of traditional industries and is conducive to the transformation and upgrading of enterprises, thereby solving the problem of high cost of building an information platform, reducing the workload of IT personnel and extending the budget Management time provides new ideas for promoting the transformation and upgrading of the Internet for small and medium-sized enterprises, which can increase the efficiency of the small and medium-sized enterprises by more than 40% and greatly increase their profits, through the intelligent sensing and communication. The research of this paper provides important guidance for the application of industrial IoT networks, especially for the intelligent sensing and communication.

Application of Industrial Internet of Things (IIoT) in the Packaging Industry in Poland

Application of Industrial Internet of Things (IIoT) in the Packaging Industry in Poland

A Fog-Based Architecture for Latency-Sensitive Monitoring Applications in Industrial Internet of Things

In industrial wireless sensors networks (IWSNs) deployments, data management and processing tasks are often delegated to centralized computing facilities. Albeit providing an abundance of resources, these solutions impose several drawbacks, stemming from the higher latency they may introduce, as well as from security/privacy issues. To deal with these shortcomings, in this article, we propose an architecture designed for industrial monitoring duties, based on the Fog computing paradigm. In particular, we devised a Fog layer to host a data aggregation algorithm based on singular value decomposition (SVD). Considering a real-world case study about a distributed monitoring system for industrial motors, the benefits of the proposed solution in terms of network performances, i.e., latency and packet delivery ratio, are analyzed using the OMNET++ simulator. Besides, the system implements a set of security mechanisms at different levels (i.e., IWSN, Fog, and Cloud). In particular, we propose a new lightweight and energy-efficient ciphering algorithm for resource-constrained industrial motes. According to our experiments, the new algorithm reduces energy consumption by a factor of three compared to Advanced Encryption Standard-128.

A Critical Survey of EEG-Based BCI Systems for Applications in Industrial Internet of Things

A Critical Survey of EEG-Based BCI Systems for Applications in Industrial Internet of Things

Trustworthiness of Process Monitoring in IIoT Based on Self-Weighted Dictionary Learning

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 not trustworthy. In order to reduce the negative impact of these unreliable factors, a self-weighted dictionary learning process monitoring method is proposed. In particular, a label propagation classifier is implemented from the labeled data to unlabeled data to obtain a credible label prediction. Subsequently, considering the interference of low-quality data and label information, we reweight the classification loss and label-consistency constraints to enhance the trustworthiness of feature extraction. Finally, a novel iterative optimization algorithm that combines the block coordinate descent method with the alternating direction multiplier method is developed to ensure the convergence speed of the learned classifier and dictionary. Extensive experiments indicate that the proposed method can guarantee the trustworthiness of the process monitoring results.

Application of Industrial Internet of Things Based Sensor Card for Asynchronous Motors

Bu çalışmada, endüstride yaygın bir şekilde kullanılan ve bu nedenle kestirimci bakım tedbirlerinin alınması önem arz eden asenkron motorlarda (ASM) meydana gelebilecek arızaların ön kestirimlerinin yapılması için endüstriyel nesnelerin interneti (IIoT) tabanlı bir sensör kartı sistemi gerçekleştirilmiştir. Gerçekleştirilen sistem 3 fazlı 250 kW gücünde ve 315L gövde yapısındaki bir ASM üzerinde denenmiştir. Bu ASM’un bulunduğu şartlar geliştirilen gömülü sistemde bulunan sensörlerle algılanmış, gömülü sistem ile bir bulut ağına aktarılmıştır. Sensör kartı uygulamasında Wi-Fi ve Bluetooth haberleşme protokollerini içeren ESP-WROOM-32 mikrodenetleyicisi, frekansa bağlı oluşan titreşim verilerini üç eksende tespit eden bir LIS3DH ivmeölçer ve sıcaklık verileri için 10 KΩ değerinde NTC ASM’un üzerine montajlanmıştır. Bu sensör verileri değerlendirilerek ASM’da oluşabilecek kestirimci bakım tespitleri yapılmış ve ASM için bu tespitler ile kullanıcılara uyarı gönderecek kestirimci bakım kararları oluşturulmuştur.

${l}\,^2$-MPTCP: A Learning-Driven Latency-Aware Multipath Transport Scheme for Industrial Internet Applications

With various industrial wireless networks greeting booming development, modern industrial devices configured with several network interfaces increasingly become the norm. Such multihomed industrial devices can increase application throughput by making use of multiple network paths, enabled by the multipath transmission control protocol (MTCP) (MPTCP). However, MPTCP might be challenged in the heterogeneous industrial networks because concurrent transmitting industrial application data over asymmetric network paths with different delays is almost bound to the receive buffer blocking problem, which is caused by out-of-order packet arrival and is harmful to the performance of the multipath transmission. The existing MPTCP solutions generally use static mathematical models to evaluate path quality and prohibit transmission on paths with poor quality, which are unable to perform efficiently under highly dynamic and complex network environments. Therefore, in this article, we propose a learning-driven latency-aware MPTCP variant, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${l}\,^2$</tex-math></inline-formula> -MPTCP, which seeks to possibly mitigate the out-of-order packet arrival and receive buffer blocking problems associated with the network heterogeneity in the industrial Internet. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${l}\,^2$</tex-math></inline-formula> -MPTCP accurately computes each MPTCP path’s forward delay and assigns application data to multiple paths according to their calculated forward delay differences by using a novel multiexpert learning-enabled forward delay estimator. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${l}\,^2$</tex-math></inline-formula> -MPTCP dynamically manages path usage and chooses the optimal path collection for bandwidth aggregation and multipath transmission by using a promising reinforcement learning-empowered multipath manager. Experimental results demonstrate that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${l}\,^2$</tex-math></inline-formula> -MPTCP outperforms the current MPTCP solutions in terms of multipathing service quality.

Application of Industrial Internet for Equipment Asset Management in Social Digitalization Platform Based on System Engineering Using Fuzzy DEMATEL-TOPSIS

In any industry, Equipment Asset Management (EAM) is at the core of the production activities. With the rapid development of Industrial Internet technologies and platforms, the EAM based on the Industrial Internet has become an important development trend. Meanwhile, the paradigm of EAM is changing, from a single machine to integrated systems, from the phase of using them to the end of their lifecycle, from breakdown maintenance to predictive maintenance, and from local decision-making to collaborative optimization. However, because of the lack of a unified understanding of the Industrial Internet platforms (IIPs) and the lack of a comprehensive reference architecture and detailed implementation framework, the implementation of EAM projects will face greater risks according to special needs in different industries. Based on the method of system engineering, this study proposes a general reference model and a reference architecture of implementation for the Industrial Internet Solution for Industrial Equipment Asset Management (I3EAM). Further, to help enterprise to evaluate and select their best-fit I3EAM scheme and platform partner, we proposed a set of performance indicators of I3EAM schemes and a quantitative decision-making method based on fuzzy DEMATEL-TOPSIS. Finally, a case study for an I3EAM in automated container terminals was conducted. In the multi-criteria decision environment with complex uncertainty, the project group identified the I3EAM metrics priorities and social digitalization platforms that were more in line with the actual needs of the automated container terminal and firms. The complexity and time of the decision-making process were dramatically reduced. In terms of feasibility and validity, the decision result was positively verified by the feedback from the enterprise implementation. The given model, architecture, and method in this study can create a certain reference value for various industrial enterprises to carry out the analysis and top-level planning of their I3EAM needs and choose the partner for co-implementation. In addition, the research results of this study have the potential to support the construction of standard systems and the planning and optimization of the cross-domain social platform, etc.

Integrated Satellite-Terrestrial Networks: Architectures, Key Techniques, and Experimental Progress

As a promising paradigm toward the sixth generation mobile communication, integrated satellite-terrestrial networks (ISTNs) can greatly extend network coverage and reduce the dependence on terrestrial infrastructure. Despite the charming potentials of ISTN, many challenges are still unsolved in terms of architectures, air interface protocol enhancement, mobility management, experimental validation, and so on. Therefore, in this article, we first clarify the four stages of ISTN architecture development and then discuss protocol enhancements as well as key techniques for ISTN. In addition, one of our previous experiments is reported in detail, where low Earth orbit satellite communication is used as the backbone connecting two private 5G networks that are 400 km away from each other. During the experiment, a remote immersive robot control service is realized with average end-to-end latency and uplink peak data rate being 30 ms and 50 Mb/s, respectively, which shows the feasibility of ISTN in supporting emerging industrial Internet applications. Meanwhile, other experimental progress is also presented, focusing on air interface protocol enhancement, transmission control protocol transmission, and network management. Finally, we point out future trends in ISTN, including programmable satellites and artificial-intelligence-based networking.

Application of Industrial Internet of Things (IIoT) in Crude Oil Production Optimization Using Pump Efficiency Control

The collapse of oil prices in mid-2014 and early 2016 was the biggest in modern history that witnessed more than a 70% drop in the price to around $40 barrel. It prompted companies to think seriously to maintain profitability. Most companies were able to survive partly by simplifying their operations. Price recovery in 2021 is only 70% of its peak value. Companies are focusing on reducing the cost of operations, increasing production simultaneously, and finding new and different strategies to survive. The current scenario is witnessing strong research focusing on the development of process control for oil and gas upstream and downstream to improve the control and preventive maintenance to reduce operating costs and increase production. This paper presents the Industrial Internet of Things (IIoT) practical solution that improves the oil production rate from the well and increases the average pump efficiency (fillage) to 90%. This paper proposes a mechanism for collecting, storing, and analyzing all required parameters to build valuable charts. These charts’ data help optimize the values and parameters for controller setpoints to prevent pump gas lock problems. An artificial lift is required to lift the oil from the well. In this paper, the sucker rod pump is driven by a gas engine fed by the well’s gas. At the same time, SCADAPack 535E remote terminal unit collects all pump and well parameters such as hydraulic pressure, casing pressure, tubing pressure, and pump speed in stroke per minute (SPM). The remote terminal unit sends the data through a wireless network using a 5 GHz antenna to the main control room. The IIoT platform is designed using a visual basic programming language. Microsoft DDE (Dynamic Data Exchange) and Kepware OPC server were used to work on the received data to monitor, generate charts, and apply the controllers.

A coupling optimization method of production scheduling and computation offloading for intelligent workshops with cloud-edge-terminal architecture

With the rapid development and application of Industrial Internet of Things (IIoT) technology, the amount of data generated during the operations of intelligent workshops shows an explosive growth trend. Real-time analysis and processing of these data is the premise of rapid intelligent decision-making. Therefore, the cloud-edge-terminal (CET) architecture is usually adopted in manufacturing sites, and the overall delay of data processing can be reduced by designing a reasonable offloading strategy for computing tasks. In general, computing tasks are generated by demand-driven monitoring, measurement, analysis, and decision-making during the executions of production jobs. There is a strong coupling relationship between them, and the scheduling scheme of production jobs largely determines the generation sequence and processing requirements of computing tasks. However, the current optimization methods for production scheduling and computation offloading are carried out separately to pursue their own goals, resulting in that the balance between production efficiency and computing delay cannot be achieved. To this end, this paper proposes a Production scheduling-Computation offloading Coupling Optimization (PCCO) model for intelligent workshops with CET architecture for the first time, which takes the maximum completion time of production jobs and the minimum total offloading delay time of computing tasks as dual optimization objectives. Then a four-layer coding method is designed and an enhanced multi-population multi-objective whale optimization algorithm (EMMOWOA) is proposed to solve the PCCO model. In EMMOWOA, the arithmetic exploration mechanism is adopted to strengthen the exploration ability, and a new leader selection method is proposed to lead the search of population to avoid prematurely trapping into the local optimum; to avoid the loss of diversity, an adaptive multi-population strategy is adopted. A series of comparative experimental results based on multi-objective standard test functions show that the proposed EMMOWOA has better convergence and diversity than the newly proposed multi-objective optimization algorithms, which proves the effectiveness of the proposed algorithm improvement strategies. Finally, simulation cases and a practical production case prove that the proposed coupling optimization method can balance production efficiency and computing delay well, and better meet the optimization requirements of intelligent manufacturing sites.

Certificateless linkable ring signature‐based blockchains for securing cognitive radio networks

SummaryRapid advancement in industrial applications of Internet of Things (IoT) has dramatically increased the number of Internet connected devices resulting in big challenges of scalability, adaptability, connectivity, and accessibility. Cognitive radio is efficient in handling spectrum allocation problem by facilitating the allocation of unlicensed spectrum dynamically to the IoT applications. However, cognitive radio network (CRN) capability of spectrum sensing is highly vulnerable to diversified number of security attacks. Malicious user disturbs the spectrum sensing potential and minimizes its accuracy by launching attacks during the detection of primary signal. In this article, certificateless linkable ring signature‐based blockchains (CLRSB) is proposed for securing CRNs by achieving reliable discrimination between trustworthy users through cryptographic keys. It initially transformed the complete set of users such as PUs and SUs into the structure of blocks representing the smart contracts‐based traditional public ledger blockchain that gets integrated to form a decentralized network for attaining improvement in efficient spectrum sensing. It adopted a mayfly optimization algorithm (MFOA)‐improved CNN (MFOA‐ICNN)‐based spectrum sensing method for preventing the interference between PUs and SUs during spectrum access. It also used MFOA for optimizing the features with balanced trade‐off between exploration and exploitation to identify the remarkable features that attributes toward efficient spectrum sensing. It specifically used CLRS for protecting information about the transaction initiator for the objective of protecting blockchain privacy. Simulation experiments of this CLRSB confirmed better response time and minimized frame loss on an average by 18.13% and 17.81%, better than the baseline schemes used for investigation.

Federated Reinforcement Learning-Based UAV Swarm System for Aerial Remote Sensing

In recent years, due to the development of technologies for unmanned aerial vehicles (UAVs), also known as drones, UAVs have developed rapidly. Because of UAVs’ high mobility and computational capability, UAVs have a wide range of applications in Industrial Internet of Things (IIoT), such as infrastructure inspection, rescue, exploration, and surveillance. To accomplish such missions, it is more proper and efficient to utilize multiple UAVs in a swarm, rather than a single UAV. However, it is difficult for an operator to understand and control numerous UAVs in different situations, so UAVs require the significant level of autonomy. Artificial intelligence (AI) has become the most promising combination with UAVs to ensure the high autonomy of UAVs by establishing swarm intelligence (SI). However, existing learning methods for building SI require continuous information sharing among UAVs, which incurs repeated data exchanges. Thus, such techniques are not suitable for constructing SI in the UAV swarm, in which communication resources are not readily available on unstable UAV networks. To overcome this limitation, in this paper, we propose the federated reinforcement learning- (FRL-) based UAV swarm system for aerial remote sensing. The proposed system applies reinforcement learning (RL) to UAV clusters to establish the SI in the UAV system. Furthermore, by combining federated learning (FL) with RL, the proposed system constructs the more reliable and robust SI for UAV systems. We conducted diverse evaluations, and the results show that the proposed system outperforms the existing centralized RL-based system and is more suited for UAV swarms from a variety of perspectives.