Journal of Industrial Information Integration最新文献

In recent times, Wireless Sensor Networks (WSNs) have become an indispensable technology across various industries, offering diverse applications and services. Among the crucial performance metrics for WSNs, optimal cluster head (CH) selection and energy efficiency are paramount for cost-effective network operations. This paper proposes a novel approach for WSNs that tackles both challenges using Zebra Fish Optimization (ZFO) and Sea Horse Optimization (SHO) algorithms. The proposed approach focuses on dynamic cluster formation and CH selection. The ZFO algorithm, enhanced with a new multi-level threading technique, dynamically selects the most suitable CH based on a fitness function. Subsequently, the SHO algorithm, equipped with an innovative adaptive parameter tuning mechanism, optimizes energy consumption within the network. This two-phased approach ensures balanced performance. Performance evaluation is validated using key metrics like packet delivery ratio (PDR), throughput, network lifetime, and residual energy. Experimental results and statistical analysis demonstrate that the proposed hybrid scheme outperforms existing popular algorithms in all these metrics. The improvements range from 1.8 % to 6.9 % for PDR, 6.7 % to 24 % for throughput, 1.86 % to 7.40 % for network lifetime, and 9.65 % to 37.95 % for residual energy. These advancements are attributed to the innovative modifications introduced in both ZFO and SHO algorithms, ultimately contributing to the enhanced performance of the entire system.

Data is a valuable resource that can facilitate the development of advanced products sustainably and transparently. However, issues like data manipulation, forgery, and deletion can damage data reliability, limiting its use during product development. In this paper, we aim to identify and explore solutions that relieve data reliability concerns throughout the life of a product. Specifically, we investigate the implementation of blockchain to mitigate data-related problems that affect product development. Blockchain is a decentralized and immutable ledger where information is inherently protected against tampering. To understand how product development can benefit from blockchain, we first identify data-dependent processes across four stages of the product’s life (i.e., design, production, distribution, and disposal) and present the challenges these processes face. We then discuss blockchain-based techniques to tackle these challenges. Finally, we outline the benefits of utilizing blockchain across the four stated stages of the product’s life. Previous surveys in this area are limited to discussing the application of blockchain to a subset of these four stages. Additionally, previous surveys do not consider the use of blockchain in materials design. In contrast, we provide a comprehensive survey that examines the utility of blockchain during the design, production, distribution, and disposal of products. Further, our survey presents how blockchain can be implemented to aid materials design. This survey provides researchers and practitioners insights into how blockchain can be applied to enhance different aspects of product design, production, distribution, and disposal.

The history of machines traces back to the dawn of human civilization, reflecting the intertwined journey of exploration, invention, and discoveries. From ancient simple machines to remarkable innovations like the Antikythera mechanism and automatons, the evolution of machines has been a testament to human ingenuity. In the 21st century, a new era of machines emerges, characterized by intelligence, connectivity, and autonomy.

The purpose of the article is two-fold. Firstly, it provides a historical view of the evolution of machines and aims to characterize their future functional attributes in a rapidly changing, technology-driven 21st-century society. Secondly, it discusses the need to prioritize the eco-centered and sustainable design of these systems as opposed to traditional performance-centered practices. To this end, the article starts by presenting a literature review on the evolution and the characteristics of future intelligent machines. We argue that there is a need to reconsider the purpose of the future intelligent machines in the anthropocentrism–ecocentrism continuum. To support this argument, the current approaches to designing these systems and the sustainability concerns related to future characteristics of them are discussed. The article concludes with remarks on different strategies aiming to provide a roadmap to aid the creators of the future connected, intelligent, and autonomous systems to maximize their positive impact and ensure continuous growth of technological development while protecting the environment and addressing societal needs.

Biomedical ontology offers a structured framework to model the biomedical knowledge in a machine-readable format. However, the heterogeneity inherent in biomedical ontologies hinders their communication. Biomedical Ontology Matching (BOM) can address this issue by identifying equivalent concepts in biomedical ontologies. Recently, Evolutionary Algorithms (EAs) based matching techniques have exhibited their effectiveness in finding high-quality matching results. However, due to the vast number of entities, and intricate relationships between entities, it is difficult for traditional EAs to efficiently solve the BOM problem. To tackle this challenge, this paper proposes an efficient BOM method to automatically match large-scale biomedical ontologies. First, a novel anchor-based biomedical ontology partitioning method is developed to transform the large-scale BOM problem into several small-scale matching tasks, reducing the search space of the matching phase. Second, a new Compact Geometric Semantic Genetic Programming (CGSGP) is proposed to efficiently construct high-level Similarity Feature for BOM, which can significantly reduce the computational complexity. Lastly, a new fitness function composed of the approximated evaluation metric and the Dominance Improvement Ratio (DIR) is introduced, which can overcome the solution’s bias improvement and enable the simultaneous matching of multiple pairs of sub-ontologies without requiring the standard alignment. The experiment verifies our approach’s performance on the Ontology Alignment Evaluation Initiative (OAEI)’s Anatomy, Large Biomed and Disease and Phenotype datasets. The experimental results show that our method can efficiently determine high-quality BOM results across different test cases, whose performance significantly outperforms the state-of-the-art BOM techniques.

Producing a Digital Twin (DT) involves many inter-linking decisions. Existing research tends to describe the parts of a DT and how they work, but not the decision-making that goes into building a DT nor the consideration of alternative design options. There is therefore a need for decision support to guide developers to create DTs efficiently while meeting functional requirements such as accuracy and interoperability. This paper presents an ontology-based decision support framework to achieve this need. Firstly an analysis of the decisions required to create a predictive maintenance DT for an automotive manufacturer is performed. The analysis found that each decision point produces an output by consideration of various influencing factors, such as time constraints, computation limits and the required fidelity of the model. The network of decisions is complex, with the outcomes of earlier decisions influencing later ones. An IDEF0 diagram was found to be a useful way to represent decisions, their dependencies and their cross-linking. This knowledge was used to populate an ontology of DT components for a predictive maintenance DT. The ability of an ontology to describe concepts explicitly using standardised vocabulary ensures the integrity of the decision-making guidance. A demonstration of the functionality of the ontology-based decision support framework was made before an evaluation of the concept. The research is a fundamental component in producing decision support for DT creators so that manufacturers can realise the benefits of a connected, responsive and flexible facility.

To effectively assess the credibility of equipment digital twins (DT) for manufacturing, a new method fully considering the dynamic evolution process is urgently needed. In this paper, the influence of the evolution process on the applications is scrutinized, the connotation of equipment DT credibility assessment is expanded, and an assessment method to evaluate the general evolution process of equipment DTs in manufacturing is proposed. The method obtains the application-time-window (ATW) by fitting a joint probability distribution based on observation of actual application time points. Then the method uses the probability of application occurrence as a weight to quantitatively assess the model performance throughout the evolution process. The effectiveness of the method was validated by ablation experiments in manufacturing simulation scenes of workpieces transferring with robotic arms. Compared with existing assessment methods, the error of quantified DT credibility value is reduced by at least 65% and the assessment result can be obtained once the evolution is completed.

Digital twin is a virtual copy of their “real-world counterparts”, which refer to physical objects, systems, or entities that exist in the physical world. They share valuable information about how the system works by connecting the replica with the real product via the internet and sensors. In digital twin organizations, industrial control systems and the internet are the main sources of data collection and synchronization, resulting in an increase in cyber-attacks. As a result, they could cause data leaks and be used by hackers to launch attacks without being detected. Researching the appropriate defenses against such attacks is of paramount interest. To that end, in this study, we build an integrated model utilizing the combination of a Dynamic Bayesian Network (DBN) and a Discrete-Time Markov Chain (DTMC) to analyze the considerable consequences of cyberattacks on a digital twin-based organization over different time period. DTMC models recovery and vulnerability for DT-based organizations, and then it is paired with the DBN model to simulate the cyberattack propagation behavior in the organization's IT performance. Next, we calculate the organization's work level and total expected utility under two different cyber-attack scenarios to determine its IT performance and financial behavior. Finally, we evaluate the organization's IT system's reliability and resilience after implementing two prevention mechanisms. Result indicates that Distributed Denial-of-Service (DDoS) cyberattack can cause more destruction to the digital twin organization's IT system than a malware attack in terms of cyberattack propagation, work and utility level, reliability, and resilience capability. The findings of the analysis hold practical applicability in real-world settings, enabling the identification of high-risk cyberattacks within DT-based organizations, the analysis and prediction of an organization's IT performance during actual cyberattacks, and the formulation of effective prevention strategies to address cyber security concerns within DT-based organizations.

This study presents a simulative tool designed to evaluate quantitatively the performance of the order picking process in a traditional warehouse with double-sided racks; performance is measured as the distance covered by the picker when carrying out a single task. Using the simulation tool, different warehouse configurations can be generated by changing both hardware (shape factor and input-output position of the picker) and software (routing policy, distribution of the demand of products and number of items in the picking list) input parameters. Then, the outcomes (distance travelled) obtained with the tool are analysed, with a particular focus on the relationship between the routing policy and the level of heterogeneity in the market demand for the products available in the warehouse, which implies changes in the product allocation. The study concludes with important results about the impact of the warehouse geometry itself on the performance of the routing policy. Finally, suggestions are offered for future studies in the field.

Electronic cash (e-cash) has become major payment way in the era of internet of things. However, the leakage of user privacy and transaction data is becoming more and more serious in e-cash system. To solve these problems, we construct a new certificateless blind signcryption from NTRU lattice (NTRUL-CLBSC) suitable for e-cash. NTRUL-CLBSC has low communication overhead and high calculation efficiency because of small storage space and short key size in number theory research unit (NTRU) lattice. Based on RSIS and DNCC problems in NTRU lattice, NTRUL-CLBSC can guarantee the anti-quantum, anti-forgery, confidentiality and blindness, and there is no key escrow and certificate management in e-cash application.

The aim of this systematic literature review is to provide a comprehensive understanding of how ontologies address current Smart Contract challenges, identify application scenarios, and present tools and technologies associated with their use. This systematic literature review (SLR), following Kitchenham's methodology, analyses peer-reviewed articles from 2015 to August 2022 from databases such as Scopus, IEEE, Science Direct, Springer Link and ACM. Of the 501 publications identified, 21 are selected for in-depth review based on inclusion, exclusion and quality assessment criteria. The results of this SLR show that ontologies provide solutions to the challenges faced by Smart Contracts mainly at the creation stage. They allow the terms of the contract and the roles of the parties to be defined. Ontologies also enable the development of Smart Contract templates. This facilitates their use by people without technical programming expertise. Despite these potential solutions to the challenges that Smart Contracts face throughout their lifecycle, they lack verification. This increases the vulnerabilities to which Smart Contracts are exposed. Developing validation and verification tools could facilitate using ontologies to create Smart Contracts for different real-world cases.