Journal of Industrial Information Integration最新文献

{"title":"A Blockchain assisted fog computing for secure distributed storage system for IoT Applications","authors":"Hemant Kumar Apat,&nbsp;Bibhudatta Sahoo","doi":"10.1016/j.jii.2024.100739","DOIUrl":"10.1016/j.jii.2024.100739","url":null,"abstract":"<div><div>With the rapid development of Internet of Things (IoT) devices, the volume of data generate across various fields, such as smart healthcare, smart home, smart transportation has significantly increased. This surge raises serious concerns about the secure storage of sensitive data for e.g., biometric information (e.g., fingerprints and facial recognition) and medical records etc. The centralized cloud computing paradigm provides various cost-effective services to IoT applications users. Despite of various benefits of centralized cloud, it fails to adequately meet the strict latency and security requirement of various IoT applications. Fog computing is proposed to enhance the real-time data processing for various latency sensitive IoT applications by extending the cloud computing services closer to the data sources. In this paper we proposed a novel blockchain based distributed fog computing model that ensures secure distributed storage for various IoT data. The blockchain network acts a trusted third party aimed at establishing secure communication among IoT devices and fog node within the fog layer. It details a distinctive Elliptic Curve Diffie–Hellman (ECDH) protocol for reliable and secure data storage and retrieval based on requests and responses from heterogeneous IoT devices. Additionally, a Merkle tree-based data structure is used to verify data integrity, ensuring secure and tamper-proof data management within the blockchain-enabled fog computing framework. It provides a formal security proof using AVISPA tools for the proposed scheme, ensuring that it meets the necessary security standards and can be trusted for protecting sensitive IoT data. Finally, the proposed scheme is compared with existing security schemes, such as AES, ABE, RSA, and Hybrid RSA in terms of resource utilization, computational cost, communication cost and execution cost. The experimental results exemplify that the proposed scheme outperform other state of the art schemes.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100739"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A digital twin-assisted intelligent fault diagnosis method for hydraulic systems","authors":"Jun Yang ,&nbsp;Baoping Cai ,&nbsp;Xiangdi Kong ,&nbsp;Xiaoyan Shao ,&nbsp;Bo Wang ,&nbsp;Yulong Yu ,&nbsp;Lei Gao ,&nbsp;Chao yang ,&nbsp;Yonghong Liu","doi":"10.1016/j.jii.2024.100725","DOIUrl":"10.1016/j.jii.2024.100725","url":null,"abstract":"<div><div>As the complexity of modern engineering systems increases, traditional fault detection models face growing challenges in achieving accuracy and reliability. This paper presents a novel Digital Twin-assisted fault diagnosis framework specifically designed for hydraulic systems. The framework utilizes a virtual model, constructed using Modelica, which is integrated with real-time system data through a first-of-its-kind bidirectional data consistency evaluation mechanism. The integrated data is further refined using a two-dimensional signal warping algorithm to enhance its reliability. This optimized twin data is then employed to train a multi-channel one-dimensional convolutional neural network-gated recurrent unit model, effectively capturing both spatial and temporal features to improve fault detection. The subsea blowout preventer in lab is used to study the performance of the method. The results show that the accuracy is 95.62 %. Compared to current methods, this is a significant improvement. By integrating DT technology, data consistency optimization, and advanced deep learning techniques, this framework provides a scalable and reliable solution for predictive maintenance in complex engineering systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100725"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced stock price prediction with optimized ensemble modeling using multi-source heterogeneous data: Integrating LSTM attention mechanism and multidimensional gray model","authors":"Qingyang Liu ,&nbsp;Yanrong Hu ,&nbsp;Hongjiu Liu","doi":"10.1016/j.jii.2024.100711","DOIUrl":"10.1016/j.jii.2024.100711","url":null,"abstract":"<div><div>The prediction of stock prices is a complex task due to the influence of various factors, high noise, and nonlinearity. This paper focuses on addressing the challenges of low prediction accuracy and poor stability, which have been a key area of interest in academic research. We proposed an optimized ensemble model that combines an LSTM-based attention mechanism and a cyclic multidimensional gray model, utilizing multi-source heterogeneous data. Our results demonstrate that the ensemble model achieves improved prediction accuracy, exhibits a good fitting effect, and outperforms individual models. The ensemble model yields smaller Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) values compared to the LSTM-based attention mechanism model and the multidimensional gray model. Furthermore, the ensemble model shows enhanced coefficient of determination (R²). Comparative analysis with alternative models such as ARIMA, GRU, CNN, and CNN-GRU reveals that the ensemble model achieves significant advancements in prediction accuracy.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100711"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging the gap: Predictive contracts in blockchain-achieving recalibration for industrial networks","authors":"Bonsu Adjei-Arthur ,&nbsp;Sophyani Banaamwini Yussif ,&nbsp;Sandra Chukwudumebi Obiora ,&nbsp;Daniel Adu Worae ,&nbsp;Olusola Bamisile","doi":"10.1016/j.jii.2024.100713","DOIUrl":"10.1016/j.jii.2024.100713","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Unfortunately, within the framework of blockchain contracting, a significant gap exists in comprehending contractual behavior, and the feasibility of predictive contracts has largely remained unexplored. A principal obstacle stems from the absence of a seamless integration between predictive concepts and blockchain technology. This deficiency is attributed to a failure to consider the inherent characteristics of blockchain when developing solutions aimed at improving predictive capabilities within blockchain-based systems. Many existing predictive approaches function externally to the fundamental blockchain framework, rendering them impractical. This has caused the idea of predictive contracts to be seen as unfeasible due to the character of blockchain smart contracts making it hard to do so. This includes its immutability and the inability for changes to be made once deployed. In this research, we introduce the concept of blockchain-based predictive contracting which stems from the theoretical idea of predictive contracting, and substantiate the feasibility of our approach, enabling blockchain smart contracts to adapt to changes in external environments upon which they depend. We attempt to achieve and prove the first phase of this idea, which we term “recalibration”. Here we provide a means for deployed smart contracts to become structurally changeable while responding to external situations without compromising their security. This we believe is the first phase needed for blockchain smart contracts before they can become predictable. Our approach capitalizes on the key-pair structure scheme utilized in existing blockchain systems to create a data signature, facilitating the identification of new smart contracts. We establish rules encompassing a configuration mechanism, empowering smart contracts to recognize newly-introduced agreements. Additionally, we implement an encoding system to enable the blockchain to respond to dynamic data. This we believe will provide a means for blockchain to be used well in industrial applications such as supply aircraft delivery networks and supply chain networks. To anticipate future scenarios, we devise a multi-versioning system that allows smart contracts to evolve over time. Our innovative concept is also demonstrated within a blockchain-based smart contract prediction scheme, ensuring the adaptability of blockchain-based smart contracts. This scheme comprises a smart contract tracing mechanism, an effective smart contract transitioning procedure, and a protocol for generating new smart contracting terms and conditions while preserving inherent trust within the system. Through extensive experimentation, involving opcode and smart contract ID extraction, Solidity Word2Vec model development, a blockchain-based embedding process, and smart contract versioning detection, we introduce the concept of blockchain-based predictive smart contracts. Notably, we observe a significant enhancement as multiple pa","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100713"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supporting business confidentiality in coopetitive scenarios: The B-CONFIDENT approach in blockchain-based supply chains","authors":"Simone Agostinelli ,&nbsp;Ala Arman ,&nbsp;Francesca De Luzi,&nbsp;Flavia Monti ,&nbsp;Michele Manglaviti,&nbsp;Massimo Mecella","doi":"10.1016/j.jii.2024.100730","DOIUrl":"10.1016/j.jii.2024.100730","url":null,"abstract":"<div><div>An important issue in <em>coopetitive</em> supply chains is ensuring business confidentiality when sharing sensitive information among partner actors. This challenge becomes even more complex in blockchain-based supply chains due to inherent transparency, conflicting with businesses’ need to safeguard sensitive information and posing risks to proprietary data. In this paper, we propose an approach based on permissioned blockchains to support transactional business confidentiality in supply chains. The approach is implemented as an open-source platform and evaluated against five non-functional requirements.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100730"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TRIPLE: A blockchain-based digital twin framework for cyber–physical systems security","authors":"Sabah Suhail ,&nbsp;Mubashar Iqbal ,&nbsp;Rasheed Hussain ,&nbsp;Saif Ur Rehman Malik ,&nbsp;Raja Jurdak","doi":"10.1016/j.jii.2024.100706","DOIUrl":"10.1016/j.jii.2024.100706","url":null,"abstract":"<div><div>Cyber–physical systems (CPSs) are being increasingly adopted for industrial applications, yet they involve a dynamic threat landscape that requires CPSs to adapt to emerging threats during their operation. Recently, digital twin (DT) technology (which refers to a virtual representation of a product, process, or environment) has emerged as a suitable candidate to address the security challenges faced by dynamic CPSs. DT has the capability of strengthening the security of CPSs by continuously mapping the physical to twin counterparts to detect inconsistencies. The existing DT-based security solutions are constrained by untrustworthy data dissemination as well as limited data sharing among the involved stakeholders, which, in turn, limit the ability of DTs to run accurate simulations or make valid decisions. To address these challenges, this paper proposes a modular framework called <strong>TR</strong>usted and <strong>I</strong>ntelligent cyber-<strong>P</strong>hysica<strong>L</strong> syst<strong>E</strong>m (TRIPLE), that leverages blockchain, DTs, and threat intelligence (TI) to secure CPSs. The blockchain-based DT components in the framework provide data integrity, traceability, and availability for trusted DTs. Furthermore, to accurately and comprehensively model system states, the framework envisions fusing process knowledge for modeling DTs from system specification-based and learning-based information and other sources, including infrastructure-as-code (IaC) and knowledge base (KB). The framework also integrates TI for future-proofing against emerging threats, such that threats can be detected either reactively by mapping the behavior of physical and virtual spaces or proactively by TI and threat hunting. We demonstrate the viability of the framework through a proof of concept. Finally, we formally verify the TRIPLE framework to demonstrate its correctness and effectiveness in enhancing CPS security.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100706"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Industrial information integration in deep space exploration and exploitation: Architecture and technology","authors":"Yuk Ming Tang ,&nbsp;Wai Hung Ip ,&nbsp;Kai Leung Yung ,&nbsp;Zhuming BI","doi":"10.1016/j.jii.2024.100721","DOIUrl":"10.1016/j.jii.2024.100721","url":null,"abstract":"<div><div>Recently, China and the United States have achieved remarkable success in aerospace science and technology over the years. Space has become another field of competition in the technological advancement of various countries. Through space missions, space tourism, moon and Mars exploration, China and the United States can demonstrate the sophistication of their technologies to the public and audiences around the world. Despite the competitiveness between the big countries, space missions and deep space exploration and exploitation have provided a lot of deep and orbital space information that is beneficial not only for the next space mission but also for enhancing technological development for other domestic uses. Therefore, space industrial information integration (III), or Space III, connecting IoT to form the Internet of Planets, is critically important for deep space explorations. However, few articles have reviewed the existing technologies of space. We are one of the few groups to perform an extensive review, research the space explorations and divide the space information integration systematically based on the information architecture and technologies in the space industries. In this paper, we propose that III can be divided into three different architectures: data, technology, and application, whereas space technology can be divided into six areas. This review is important not only in formulating research in technological integration but also in determining the proposed architecture to facilitate a further extension of applications to large-scale and complex problems in the space industries in the future.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100721"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model management to support systems engineering workflows using ontology-based knowledge graphs","authors":"Arkadiusz Ryś ,&nbsp;Lucas Lima ,&nbsp;Joeri Exelmans ,&nbsp;Dennis Janssens ,&nbsp;Hans Vangheluwe","doi":"10.1016/j.jii.2024.100720","DOIUrl":"10.1016/j.jii.2024.100720","url":null,"abstract":"<div><div>System engineering has been shifting from document-centric to model-based approaches, where assets are becoming more and more digital. Although digitisation conveys several benefits, it also brings several concerns (e.g., storage and access) and opportunities. In the context of Cyber-Physical Systems (CPS), we have experts from various domains executing complex workflows and manipulating models in a plethora of different formalisms, each with their own methods, techniques and tools. Storing knowledge on these workflows can reduce considerable effort during system development not only to allow their repeatability and replicability but also to access and reason on data generated by their execution. In this work, we propose a framework to manage modelling artefacts generated from workflow executions. The basic workflow concepts, related formalisms and artefacts are formally defined in an ontology specified in OML (Ontology Modelling Language). This ontology enables the construction of a knowledge graph that contains system engineering data to which we can apply reasoning. We also developed several tools to support system engineering during the design of workflows, their enactment, and artefact storage, considering versioning, querying and reasoning on the stored data. These tools also hide the complexity of manipulating the knowledge graph directly. Finally, we have applied our proposed framework in a real-world system development scenario of a drivetrain smart sensor system. Results show that our proposal not only helped the system engineer with fundamental difficulties like storage and versioning but also reduced the time needed to access relevant information and new knowledge that can be inferred from the knowledge graph.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100720"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extended material requirement planning (MRP) within a hybrid energy-enabled smart production system","authors":"Rekha Guchhait ,&nbsp;Mitali Sarkar ,&nbsp;Biswajit Sarkar ,&nbsp;Liu Yang ,&nbsp;Ali AlArjani ,&nbsp;Buddhadev Mandal","doi":"10.1016/j.jii.2024.100717","DOIUrl":"10.1016/j.jii.2024.100717","url":null,"abstract":"<div><div>A smart production system can be made energy-efficient using renewable energy and is considered to maintain the extended material requirement planning under a logistics system by using radio frequency identification. The tracking technology provides information about products with real-time notification. This study investigates renewable energy usage within a smart production system as renewable energy can contribute to Net Zero Emissions. The logistics framework involves an autonomation technology-based production system, optimum cash flow, logistics, and carbon emissions. Time is an essential influencer for material requirement planning. The model is solved with a Laplace integral transformation, where an associated matrix method is utilized by the input–output analysis. The theoretical concept is elaborated through an illustrative numerical example, where the energy consumption and corresponding net present values are evaluated. Numerical and graphical studies prove the effectiveness of the model for the use of renewable energy within for material planning under a reverse logistics system. The result reveals that efficient renewable energy consumption can save considerable costs and reduce the negative net present value of the system. It is found that skilled workers are worthy of a smart production system, not only in a qualitative aspect but also in an economic aspect.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100717"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Making data classification more effective: An automated deep forest model","authors":"Jingwei Guo ,&nbsp;Xiang Guo ,&nbsp;Yihui Tian ,&nbsp;Hao Zhan ,&nbsp;Zhen-Song Chen ,&nbsp;Muhammet Deveci","doi":"10.1016/j.jii.2024.100738","DOIUrl":"10.1016/j.jii.2024.100738","url":null,"abstract":"<div><div>Despite a small overfitting risk, the deep forest model and its variants cannot automatically match data features; they rely on manual experience and comparative experiments for forest learner selection. This study proposes an automated deep forest model (ATDF) to enhance deep forest automation by automatically determining forest learners’ types and numbers based on training data. The model introduces a forest learner variability measure based on normalized mutual information, serving as a theoretical foundation for the automated process in deep forests. Then, a novel hierarchical clustering algorithm based on normalized mutual information is proposed to group forest learners at different granularities, determining the optimal forest learner type. This advanced technical method enables the determination of the model structure for stacking models, including deep forests. Finally, with the goal of maximizing cross-validation scores, the tree parson estimator-based Bayesian optimization algorithm determines the ideal number of forest learners for each type. Additionally, a standardized method for identifying forest learners is developed to guarantee the consistency of model outcomes. Most importantly, a series of comparative experiments on seven datasets from the UCI Machine Learning Repository confirmed the effectiveness and superiority of the proposed model. The results demonstrate that the proposed model has superior adaptability to new data and tasks, besides having a high level of automation, and performs excellently in the classification task.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"42 ","pages":"Article 100738"},"PeriodicalIF":10.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}