Industrial Cloud Research Articles

Towards a geographical political economy understanding of platformization

In the last ten years digital platforms have emerged as an important theme in academic discourse and various subdisciplines of geography and adjacent research areas. Whether in the fields of social media, urban transport, industry cloud services or gig work, platformization is therein often discussed as a more-than-economic phenomenon engaging in a conflictive relationship with public interest, sovereignty, regulation and the state. However, while the concept of the platform has become ubiquitous, the integration of and dialogue between various broadened definitions are less often practiced. In our short paper, we argue that for further addressing this intricate relation between platforms, power and sovereignty an engagement with the platform concept and its integration through a political economy understanding is key for two reasons: Firstly, an overarching concept of platformization transcending the political/economy binary is necessary to be able to problematize the reification of normalized economic practices lying at the heart of the phenomenon. Secondly, there is a pragmatic reason for developing a geographical political economy perspective on platformization as it can serve as a common ground for an engaged pluralism within and beyond the discipline of economic geography. Based on a systematization of existing definitions this paper therefore combines insights from political economy perspectives, urban platform research and research on platform labor to propose a working definition stimulating a reflection on the platform discourse thus far and strengthening strategical-relational thinking on platform power in future research.

An Oversubscription and Service Pricing Exploitation-Based Profit Maximization Framework for Industry Cloud Resource Management

An Oversubscription and Service Pricing Exploitation-Based Profit Maximization Framework for Industry Cloud Resource Management

Towards consensual representation: Model-agnostic knowledge extraction for dual heterogeneous federated fault diagnosis

Federated fault diagnosis has attracted increasing attention in industrial cloud–edge collaboration scenarios, where a ubiquitous assumption is that client models have the same architecture. Practically, this assumption cannot always be fulfilled due to requirements for personalized models, thereby resulting in the problem of model heterogeneity. Many approaches dealing with heterogeneous models tend to neglect the issue of representation bias, particularly in the context of non-identically and independently distributed data. In this article, to address the representation bias problem, Federated Model-Agnostic Knowledge Extraction (FedMAKE) is proposed. To bridge the information gap among clients, different from methods with public datasets, we initially develop two novel architecture-independent knowledge carriers. These carriers are derived based on the importance of process variables, without the need for additional datasets. Subsequently, we introduce a bi-directional distillation algorithm utilizing the two knowledge carriers. This algorithm facilitates the mutual transfer of knowledge embedded in carriers between a generative network and client models, thereby enabling the generation of fault data that is unbiased and well-balanced across categories. Furthermore, to mitigate the impact of statistical heterogeneity, we formulate a local objective for each client using two global knowledge carriers to guide local knowledge extraction and constrain client drift. Extensive experiments conducted on two prevalent industry datasets (TE and CWRU) illustrate that our proposed FedMAKE outperforms baseline methods. Specifically, FedMAKE enhances fault diagnosis accuracy by up to 11.7% on the TE dataset and up to 3.31% on the CWRU dataset compared to the sub-optimal method.

EKF Connect Industry digital platform for efficient operation of manufacturing plants

The decline in the supply of digital solutions for monitoring and supervisory control of technological processes in the domestic market has given Russian companies an incentive to begin developing their own cost-effective information systems and cloud solutions. EKF experts have developed the EKF Connect Industry Internet of Things platforms. The IIoT analytical platform has become part of the EKF Connect cloud product ecosystem. The EKF Connect Industry cloud platform is a smart solution for industrial enterprises that need process control, which does not require the purchase of expensive additional equipment, and does not require additional highly qualified specialists to maintain it.

Understanding the operators’ cloud change errors based on cognitive abilities and personality traits: An investigation integrated with quantitative and qualitative methods

While cloud services make industrial data convenient, they also expose it to cloud incidents. Operators' error in cloud change activities is a leading factor for cloud incidents, which have received relatively less attention in cloud security research. This study conducted a two-stage research process using an integrated approach to explore the stable individual factors related to cloud change errors. First, in the qualitative research, content analysis based on interviews and historical documents was conducted to extract the operator's cognitive abilities and personality traits and develop hypotheses. Five cognitive abilities and six personality traits were extracted. Second, quantitative research based on an experiment was conducted to test relationships between operators' different types of cloud change errors and 1) cognitive ability and 2) personality traits, respectively. Results of error type comparisons suggested that operators generated more uncorrected errors than corrected errors and more operational errors than omission errors in cloud change activities. The multivariate Poisson regression analysis suggested that cognitive abilities of sustained attention, divided attention, and long-term memory negatively predicted the number of operators' total errors, uncorrected errors, and operational errors. Regarding personality traits, with the increase in resilience capacity and carefulness and the decrease in self-esteem, the number of different types of errors reduced, except for omission errors. Working memory and risk-taking propensity were also significant predictors of the number of uncorrected errors with negative and positive coefficients, respectively. Logical reasoning, emotional stability, and sense of responsibility were not observed as predictors of cloud change errors. The present findings have several implications for the industry and cloud providers to enhance industrial cloud data security regarding human cognitive abilities and personality traits.

Federated knowledge amalgamation with unbiased semantic attributes under cloud–edge collaboration for heterogeneous fault diagnosis

Federated fault diagnosis attracts increasing attention in industrial cloud–edge collaboration scenarios due to the consideration of privacy and security issues. However, statistical heterogeneity caused by non-independent and identically distributed (non-IID) fault data is a ubiquitous problem. Previous methods designed for this problem mainly apply weighted averaging or knowledge distillation to integrate biased local knowledge from clients, ignoring the discrepancy between knowledge extracted from non-IID data solely. This is prone to result in slow and unstable model convergence and global model drift. In this article, a Federated Knowledge Amalgamation (FedKA) framework is proposed to alleviate the knowledge discrepancy caused by statistical heterogeneity. To mitigate the knowledge discrepancy, we first reveal and mine unbiased public knowledge for industrial fault diagnosis, i.e., fault attributes, which describe the fault characterization and mechanism without changing with data distribution. Then, a generative mapping between unbiased fault attributes and fault data is established for clients to incorporate unbiased knowledge into the diagnostic model. Moreover, to mitigate knowledge discrepancy and overcome the global model drift, a knowledge amalgamation algorithm is designed. Through the proposed layer-wise alignment, the multi-level fault semantic knowledge that stems from fault attributes is finely exploited and aggregated. Additionally, unlike the common fixed weighting strategy, we explore an adaptive weighting mechanism through the sample-wise diagnostic confidence to further alleviate the global model drift. Extensive experiments on a real thermal power plant process demonstrate the superiority of the proposed method for federated fault diagnosis.

Design of Industrial Edge Applications Based on IEC 61499 Microservices and Containers

Industrial automation is entering a new era of the Industrial Internet with enhanced computing, communication, and storage capabilities provided by cloud computing and field devices. The paradigm of automation systems is shifting from the ISA-95 pyramid to the two-layers architecture: industrial cloud and edge computing. Industrial software is also evolving under the new architecture in ways for which dedicated software applications are no longer suited. Service-based industrial cloud and edge applications provide maximum flexibility, interoperability, and efficiency by combining the IEC 61499 standard, microservice architecture, and container technology. This paper provides orchestration methods and deployment procedures for the OT-IT hybrid industrial edge applications. The feasibility of the proposed approach is demonstrated by an industrial case study with accompanying performance analysis.

Enhancing Industrial Cyber Security, Focusing on Formulating a Practical Strategy for Making Predictions through Machine Learning Tools in Cloud Computing Environment

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study’s objectives. This study identifies gaps in utilizing ML techniques in cloud cyber security. Moreover, this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of precision, accuracy, and recall values, as well as F1 scores, R.O.C. curves, and confusion matrices. The results demonstrated that the X.G.B. model outperformed, in terms of all the matrices, with an accuracy of 97.50%, a precision of 97.60%, a recall value of 97.60%, and an F1 score of 97.50%. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.

Sensitive product feature integrity and confidentiality using blockchain-based internet of things (IoT) architecture

Small-scale third-party accessories vendors face numerous challenges such as (i) warranty period covered for sensitive product, (ii) malicious induction, and (iii) extract root cause of product failure after customer experienced in the real time usage. Therefore, an advanced industrial environment is necessary to resolve these limitations. It offers strong security levels as well as intelligent root cause detection over failure. In this work, propose a blockchain-based internet of things (IoT) architecture for industry 4.0, which includes a voting decision mechanism for IoT device authentication and fault classification based on device and product trust values, respectively. These values are regularly updated in the historical trust value and machinery database for every successful product transaction and identify a new failure cause to set feature for next iteration set from the defective industrial cloud database. The analytical model and simulation results for correct authentication ensure that the proposed architecture achieves 86% eligibility of accuracy across a high ratio of malicious attacks in less converged time.

Enabling blockchain-assisted certificateless public integrity checking for industrial cloud storage systems

Due to the sharp increase in industrial data, cloud computing has been integrated into intelligent Industrial Internet of Things systems to store and process massive data. Since core data are key factors in the industrial production process, any intercepted or altered data may cause severe consequences, and the integrity and confidentiality of outsourced industrial data have become extremely concerning security issues. In this paper, we focus on devising an enhanced blockchain-assisted certificateless public integrity checking (BA-CPIC) mechanism for industrial cloud storage systems. BA-CPIC contributes to checking outsourced industrial encrypted data, and exploits the smart contract in the Ethereum blockchain to provide assistance to users for checking malicious behaviors of auditors (e.g., colluding with industrial cloud servers or lazing checking). Besides, BA-CPIC preserves the identity privacy of users enjoying outsourced storage and blockchain-assisted integrity checking services. By contrast, some misbehaved users in industrial cloud storage systems could also be traced and revoked in due course. BA-CPIC avoids both inherent drawbacks of key escrow and complex certificate management, it is provided corresponding security analysis under the certificateless security model. The performance evaluation demonstrates the feasibility of BA-CPIC in the deployment of industrial cloud storage systems.

Fountain Codes for Reliable and Deterministic Packet Transmission in Industrial Cloud Control Systems

With the thriving of digitalization in industry and the rapid development of cloud computing and Internet of Things (IoT), it has become a growing trend to integrate industrial control with the cloud platform to form industrial cloud control systems (ICCSs). However, ICCSs bring great challenges to legacy communication networks, especially, wireless networks, on reliability and determinacy. This article utilizes fountain code as application-layer coding to provide high-reliability low-latency deterministic communication for ICCSs. According to the simulation results, fountain code scheme, especially, RaptorQ code significantly improves communication reliability. The performance difference of fountain codes between time-critical use cases and nontime-critical use cases provides another perspective to analyze and evaluate fountain codes. Then, we propose a self-adaptive coding parameter selection method based on different industrial use cases, to better meet the communication requirements and reduce the coding cost.

Automatic Information Model Generation for Industrial Edge Applications Based on IEC 61499 and OPC UA

The Industry 4.0 and Industrial Internet provides vertical and horizontal integration between edge devices and industrial cloud platforms. A flexible and interoperable information model is crucial for enabling device-level intelligence as well as cloud-edge collaboration. Deep integration between the control and information model is necessary for supporting closed-loop optimization between the cloud and edge devices. This article proposes an automatic information model generation method based on the IEC 61499 and OPC unified architecture standards. Model transformation rules between two models from design time to runtime are presented. A discrete manufacturing case study is used to prove that the proposed method can significantly reduce the development time for distributed information models. The integrated control and information models at runtime can provide strong support for dynamic reconfiguration to address changing requirements.

Study on concentration distribution and detonation characteristics for non-axisymmetric fuel dispersal

The study of non-axisymmetric fuel dispersal and detonation can provide reference for the prevention of industrial cloud explosion accidents and the design of fuel air explosive (FAE). The concentration and detonation fields of 85 kg cylindrical and fan-shaped fuel are investigated by experiments and numerical simulations. A dynamic model of the whole process for fuel dispersal and detonation is built. The concentration distribution of fuel is used as the initial condition to calculate the detonation stage, thus solving the initial value problem of detonation field. The phase and component changes of fuel cloud at different locations are compared. The fuel cloud is divided into directions of 0°, 90°, 135° and 180°. The results show that the maximum cloud radius is 20.94 m in 135° and the minimum is 12.04 m in 0°. The diameter of the detonation fireball is 53.6 m, and the peak temperature is 3455 K. The highest peak overpressure is 3.44 MPa in 0° and the lowest is 2.97 MPa in 135°. The proportion of liquid phase in 0° is 22.90%, and the fuel loss is 11.8% and 9% higher than that in 135° and cylindrical charge, respectively. The stable propagation distance of blast wave in 135° is 42.50% longer than 0° and 28.37% longer than cylindrical charge.

Parallel Scheduling of Large-Scale Tasks for Industrial Cloud–Edge Collaboration

Industrial Internet of Things is moving toward an intelligent level with large-scale collaborative cloud and edge resources, making it possible for online supervision, fast analysis, and precise control for many manufacturing job shops. However, online processing of large-scale industrial computation brings huge communication overhead and energy consumption among cloud, edge, and end devices. To improve the performance of the cloud–edge collaboration, this article establishes a practical model of task scheduling considering two kinds of cloud–edge collaborative modes. We propose a parallel group-merge evolutionary algorithm to assign thousands of tasks in seconds. The algorithm separates tasks into weakly correlated groups and applies modified evolutionary operators to find a subsolution for each group. Then, the subsolutions are merged to form a complete solution for fine-tuning based on the cross-use of heuristics. Experimental results show that the proposed method could assign thousands of tasks to cloud servers and edge servers in seconds, reduce the overall task computing time by 36.97%, and save the overall energy by 23.71% at most.

The Potential of Low-Power, Cost-Effective Single Board Computers for Manufacturing Scheduling

The ongoing fourth industrial revolution is pushing manufacturing towards smart, cognitive, and more adaptable systems. These new paradigms will require a huge number of computational resources. To meet these needs, the evolution of Industrial Internet added a layer of low-power heterogeneous devices with processing capabilities on the edge of the network, closer to the area where it is needed. Single board computers are the most common hardware platform found in the edge nodes. Due to its computation performance and power efficiency, the resources of this layer on the edge should be explored to solve processing demanding tasks. Maybe systems like the enterprise resource planning systems (ERP) and the manufacturing execution systems (MES) that are typically hosted in the industrial cloud can take advantage of sharing services between the cloud and the edge. This work aims to explore the edge layer collaboration with the cloud, sharing computational power to process the manufacturing scheduling problems linked to the ERP and MES. Therefore, in this preliminary study, we investigate the potential of single board computers to solve manufacturing scheduling problems. We present the experiments with four versions of Raspberry Pi, a well-known single board computer, evaluating them from the energy consumption and computation time points of view. Experiments show that it is possible to solve these scheduling problems in a reasonable time, with a lower energy consumption than other high-end computing devices.

Design of Control System for Industrial Defective Product Sorting Station Based on IOT2050

Aiming at the problems of many types of abnormal states in industrial defective sorting stations, long time consuming analysis of abnormal causes, and untimely equipment monitoring, the control system of industrial defective sorting stations based on IOT2050 is designed. With Siemens PLC-1200 as the main body, combined with TIA Portal and IOT2050, it realizes the design of software and hardware configuration, programming, WinCC screen and industrial cloud platform of industrial defective sorting station control system. The simulation results show that the system runs stably, realizes automatic production operation and data monitoring, and is of great significance to ensure the reliable operation of equipment.

Cloud-IIoT-Based Electronic Health Record Privacy-Preserving by CNN and Blockchain-Enabled Federated Learning

Industrial cloud computing and Internet of Things have transformed the healthcare industry with the rapid growth of distributed healthcare data. Security and privacy of healthcare data are crucial challenges in the healthcare industry. This article proposes a novel technique using deep learning and blockchain techniques for electronic health record privacy-preservation. The processed dataset classified normal and abnormal users using the convolutional neural network approach. Then, by using blockchain integrated with a cryptography-based federated learning module, the abnormal users have been processed and removed from the database along with the accessibility for the health records. The simulation has been done in the Python tool and experimental results show that the model’s classification results and performance are better than other existing techniques.

Learning-Based Edge Sensing and Control Co-Design for Industrial Cyber–Physical System

The new generation of edge computing supported industrial cyber–physical system (ICPS) promotes the deep integration of sensing and control. The unknown model is one of the key challenges to characterize their interactions. In most existing works, many efforts have been devoted to overcoming the challenge for the single aspect of sensing and control. However, the industrial revolution puts forward the higher requirements of the overall production performance. To solve this problem, we propose a novel framework for learning-based edge sensing and control co-design. Specifically, the model learning error is first analyzed to bound the actual control performance. Then, the bound is further linked to the sensing design through the bridge of relaxed assumptions of the nonzero initial state and unknown order. Besides, the cloud-edge symphony (CES) algorithm is designed for the co-design problem solving considering the defects of the single edge computing unit (ECU). In the novel framework, the processes of sensing, control, and learning are comprehensively considered for global optimization. Finally, the proposed algorithm is applied to the personalized production of laminar cooling based on the semiphysical evaluation, and the effectiveness is verified by the results. Note to Practitioners—Edge computing supported ICPS deeply integrates the sensing and control processes. It is beneficial to realize the small-batch customized production for the individual demands in intelligent manufacturing. However, the inevitable problem of weak prior knowledge of system models motivates us to adopt appropriate learning methods to deal with the model inaccuracy and characterize the internal relationship between sensing, control, and model learning. In this article, we propose a novel framework to comprehensively consider the performance of different aspects for global optimization. Specifically, the relaxed assumptions of the nonzero initial state and unknown order are regarded as the bridge to combine edge sensing and control. The cloud-edge symphony (CES) algorithm is proposed to solve the co-design problem and applied to the laminar cooling process for evaluation. It is observed that better overall performance is achieved than previous methods. In the future, our framework can be further extended from the single edge computing unit (ECU) and collaboration with the industrial cloud platform to coordinate sensing and control between the multiple ECUs. Besides, the production requirements of specific applications can be further considered including the real-time response and the reuse of production experience.

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

The prognostics health management (PHM) from the systematic view is critical to the healthy continuous operation of process manufacturing systems (PMS), with different kinds of dynamic interference events. This paper proposes a three leveled digital twin model for the systematic PHM of PMSs. The unit-leveled digital twin model of each basic device unit of PMSs is constructed based on edge computing, which can provide real-time monitoring and analysis of the device status. The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters, which are deployed for the manufacturing execution on the fog server. The shop-leveled digital twin maintenance model is designed for production planning, which gives production instructions from the private industrial cloud server. To cope with the dynamic disturbances of a PMS, a big data-driven framework is proposed to control the three-level digital twin models, which contains indicator prediction, influence evaluation, and decision-making. Finally, a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs. The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system's respects.

Multi-granularity service composition in industrial cloud robotics

• A multi-granularity service composition model in industrial cloud robotics is built considering both fine-grained and coarse-grained robotic services. • A multi-granularity service matching strategy is designed based on service matching constraints. • Six multi-objective evolutionary algorithms are adopted and compared for the multi-granularity service composition problem . Industrial cloud robotics employs cloud computing technology to provide various operational services, such as robotic control modules that enable customized screwing and welding. Service composition technology enables the flexible implementation of complex industrial robotic applications based on the collaboration of multiple industrial cloud robotic services. Most studies considered cloud robotic services with a single robotic manipulator with a fixed function. To utilize the advantages of coarse-grained services encapsulated by multi-functional robots, manipulators, and control applications, a multi-granularity service composition method is introduced considering the multi-functional resources and capabilities of the cloud robotic services. Then a quality-of-service-aware multi-granularity robotic service composition model is built to evaluate the composition solution. Furthermore, a multi-granularity robotic service matching strategy is proposed according to the matching constraints of coarse-grained services. Six representative multi-objective evolutionary algorithms are adopted to optimize five quality-of-service attributes of the composite service simultaneously. Experiments demonstrate that the proposed multi-granularity robotic service composition method can remarkably improve the quality of robotic composite services for complex manufacturing tasks by utilizing coarse-grained services in addition to fine-grained services. The performances of six multi-objective evolutionary algorithms are compared to determine the most suitable algorithm for the multi-granularity robotic service composition problem.