A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools

Xu, Hansong; Wu, Jun; Pan, Qianqian; Guan, Xinping; Guizani, Mohsen

doi:10.1109/comst.2023.3297395

Cited by 33 publications

(6 citation statements)

References 206 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can include how the data is formatted or where it is physically discovered, which reduces capital and operating costs, minimizes or eliminates downtime, increases information productivity, efficiency, agility, and responsiveness, and speeds up the provisioning of applications and resources. Virtualization in its novel form generates digital twins, which are a virtual representation of objects or systems that span their life cycle, are updated from real-time data, and using simulations, AI, ML, KG, and reasoning to help decision-making [107,109]. This recent challenge of virtualization needs digital twin modeling, which is an emerging and vital technology for digital transformation and intelligent upgrade [108].…”

Section: Data Ecosystem Modelmentioning

confidence: 99%

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Mahabadi,

Abdolazimi

2024

Preprint

View full text Add to dashboard Cite

The classification of large image data using traditional methods has been based on changes in image gray features, extraction of edge and contour feature information, or conversion between image coordinate sets. However, due to the growth of new image size and big image data (BID) in real-time multimedia communication and future online big data (BD) applications, these methods have become increasingly complex and resulting in poor real-time performance. These complex methods suffer from complex algorithms, massive data communication, slow processing speed, unintelligent predictive modeling, weak data classification, limited accuracy, uncleaned usage data, combined destructive artificial and natural noise, exchanging data values over time, and exhausting updating data for accurate operation of media storage. In the era of the Internet of Multimedia Things (IoMT), most modern devices can accurately capture vast amounts of observational data in the form of valuable images, text, and acoustic recordings. This reliable data must be kept valid for local data processing at different times and for globally extracting knowledge from information at various data levels to address forthcoming challenges related to big image data. The challenges of secure communication require accurate, reliable, fast, and effective information gathering for local decision-making and global knowledge mining of BID to support cyber-physical systems for speedy data virtualization of smart city devices. Complex and uncontrollable problems persist on the outskirts of grids, fogs, and cloud networks regarding data cleansing and privacy protection for intelligent data ocean management. This study developed a new 15Vs model empirically to examine distributed big image data processing based on a modern BID connectivity approach. The model accurately extracts textured features from visible data images and overcomes a unique set of key challenges. The model introduces a strategic analyzer, uses intelligent local agents, and recruits clever global bots to provide a suitable platform for generous support of bot-oriented BD processing. This instantly forms a hierarchical data level for better data acquisition and cleansing, safe privacy protection, suitable information diffusion, and speedy knowledge extraction. Our study presents a novel definition of BID to address the ultimate challenges of data management in a high-risk environment for further big image data modeling research needs.

show abstract

Section: Data Ecosystem Modelmentioning

confidence: 99%

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Mahabadi,

Abdolazimi

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…unable to be changed) and verifiable (i.e. can be checked using methods) nature of blockchain makes it ideal for enhancing traceability and authentication in the automotive aftermarket where consumers may verify the source and record of the spare parts and accessories through blockchain [28]. As with auto systems that are gradually turning to be more connected and autonomous, blockchain technology is reckoning to be a main revolution heading to eradicate the various aspects of the auto sector for example vehicle ownership, supply chain management, and aftermarket services which will ultimately bring about efficiency, transparency, and trust.…”

Section: Literature Reviewmentioning

confidence: 99%

Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network

Javaid Ahmad Malik

2024

pst

View full text Add to dashboard Cite

Smart Car's era ushers in new challenges, some of which are in the field of information security such as wise cyber-attack prevention. Traditional IDS systems would lose everything in such a dynamic environment with their centralized architectures, and this approach could create single points of failure and privacy issues. Along with interconnectedness, cyber security becomes an inevitable problem as smart vehicles are incorporated into a daily life. Traditional security mechanisms usually lack scalability and privacy, which brings about the need to develop alter-nate or innovative methods. This research demonstrates a mixed security system that combines both federated learning and blockchain technologies to improve intrusion detection in smart vehicular networks. We evaluated the effectiveness of this framework using four machine learn-ing models as respectively; Support Vector Machine (SVM), Decision Tree, Neural Network, and Random Forest. Empirical results show that SVM had the highest accuracy of both 93.88% in training and 91.84% in validation, which is higher than Decision Tree, Neural Network, and Random Forest models. These findings evidently demonstrate that the federated learning and blockchain are a strong solution for the plausible security of smart vehicle networks; with SVM being employed mostly in complex security scenarios. DOI: https://doi.org/10.52783/pst.353

show abstract

“…A digital twin represents the physical object, process, or system in virtual representation [32]. It includes both the physical and digital domains through duplicated real-time data from the physical system with a simulation model to enhance its physical counterpart's behavior, characteristics, and performance [33].…”

Section: Digital Twin Fundamentals and Application In Ambmentioning

confidence: 99%

Fault Detection in Active Magnetic Bearings Using Digital Twin Technology

Hu,

Taha,

Yang

2024

Applied Sciences

View full text Add to dashboard Cite

Active magnetic bearings (AMBs) are widely used in different industries to offer non-contact and high-velocity rotational support. The AMB is prone to failures, which may result in system instability and decreased performance. The efficacy and reliability of magnetic bearings can be significantly affected by failures in the sensor and control systems, leading to system imbalance and possible damage. A digital twin is an advanced technology that has been increasingly used in different industrial fields. It allows for the creation and real-time monitoring of virtual replicas of physical systems. This paper proposes a novel method for fault detection of Active Magnetic Bearings (AMBs) using digital twin technology and a neural network. The digital twin model serves as a virtual representation that accurately replicates the actual AMB system’s efficiency and features, allowing continuous real-time monitoring and detection of faults. The conventional neural network (CNN) is used as the primary tool for identifying faults in the Active Magnetic Bearing (AMB) within a digital twin model. Experiments proved the effectiveness and robustness of the suggested approach method to fault detection in the AMB.

show abstract

A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools

Cited by 33 publications

References 206 publications

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Big Image Data 15Vs Model for Intelligent Data Ocean of Multimedia Things

Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network

Fault Detection in Active Magnetic Bearings Using Digital Twin Technology

Contact Info

Product

Resources

About