Digital Twin for 5G and Beyond

Nguyen, Huan X.; Trestian, Ramona; To, Duc; Tatipamula, Mallik

doi:10.1109/mcom.001.2000343

Cited by 269 publications

(109 citation statements)

References 10 publications

Supporting

Mentioning

107

Contrasting

Unclassified

Order By: Relevance

“…In the present work, MATLAB is adopted for simulation to validate the performance of the proposed model. (Nguyen et al, 2021), CNN, RNN (Minerva et al, 2021), AlexNet, and MLP (Dong et al, 2019). Experimental environment configuration includes software and hardware.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

et al. 2021

View full text Add to dashboard Cite

The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins. Moreover, a diagnosis and prediction model of brain image fusion digital twins based on semi supervised SVM and improved AlexNet is constructed. Magnetic Resonance Imaging (MRI) data from the Brain Tumor Department of a Hospital are collected to test the performance of the constructed model through simulation experiments. Some state-of-art models are included for performance comparison: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), AlexNet, and Multi-Layer Perceptron (MLP). Results demonstrate that the proposed model can provide a feature recognition and extraction accuracy of 92.52%, at least an improvement of 2.76% compared to other models. Its training lasts for about 100 s, and the test takes about 0.68 s. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed model are 4.91 and 5.59%, respectively. Regarding the assessment indicators of brain image segmentation and fusion, the proposed model can provide a 79.55% Jaccard coefficient, a 90.43% Positive Predictive Value (PPV), a 73.09% Sensitivity, and a 75.58% Dice Similarity Coefficient (DSC), remarkably better than other models. Acceleration efficiency analysis suggests that the improved AlexNet model is suitable for processing massive brain image data with a higher speedup indicator. To sum up, the constructed model can provide high accuracy, good acceleration efficiency, and excellent segmentation and recognition performances while ensuring low errors, which can provide an experimental basis for brain image feature recognition and digital diagnosis.

show abstract

Section: Simulation Experimentsmentioning

confidence: 99%

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Another key driving feature of Digital Twins in the automation and manufacturing industries is industrial wireless networks. The potential advantages from wireless technologies are significant and they have been studied through papers that elaborate on the importance of the industrial wireless [22] and 5G environment [41]- [43] for smart manufacturing. Beside conceptual works, some recent study investigates Digital Twin solutions over IoT communication technologies (LTE-M, LoraWan and Sigfox) for smart manufacturing assembly systems [44].…”

Section: B Applications In Real Industrial Environmentsmentioning

confidence: 99%

Dissecting the Impact of Information and Communication Technologies on Digital Twins as a Service

et al. 2021

View full text Add to dashboard Cite

Recent advances on Edge computing, Network Function Virtualization (NFV) and 5G are stimulating the interest of the industrial sector to satisfy the stringent and real-time requirements of their applications. Digital Twin is a key piece in the industrial digital transformation and its benefits are very well studied in the literature. However, designing and implementing a Digital Twin system that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency, reliability) is an ambitious task. Therefore, prototyping the system is required to gradually validate and optimize Digital Twin solutions. In this work, an Edge Robotics Digital Twin system is implemented as a prototype that embodies the concept of Digital Twin as a Service (DTaaS). Such system enables real-time applications such as visualization and remote control, requiring low-latency and high reliability. The capability of the system to offer potential savings by means of computation offloading are analyzed in different deployment configurations. Moreover, the impact of different wireless channels (e.g., 5G, 4G and WiFi) to support the data exchange between a physical device and its virtual components are assessed within operational Digital Twins. Results show that potentially 16% of CPU and 34% of MEM savings can be achieved by virtualizing and offloading software components in the Edge. In addition, they show that 5G connectivity enables remote control of 20 ms, appearing as the most promising radio access technology to support the main requirements of Digital Twin systems.

show abstract

“…Due to the fast development of telecommunication technologies, 5G and beyond networks will be very complicated as they are expected to support more emerging applications with more diverse requirements [33]. The community is considering DT as an efficient, cost-effective approach to accelerate the design, test, and implementation of 5G/6G networks [34].…”

Section: Digital Twinsmentioning

confidence: 99%

Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)

Qu¹,

Xu²,

Chen³

et al. 2021

Preprint

View full text Add to dashboard Cite

Blockchain technology has been recognized as a promising solution to enhance the security and privacy of Internet of Things (IoT) and Edge Computing scenarios. Taking advantage of the Proof-of-Work (PoW) consensus protocol, which solves a computation intensive hashing puzzle, Blockchain assures the security of the system by establishing a digital ledger. However, the computation intensive PoW favors members possessing more computing power. In the IoT paradigm, fairness in the highly heterogeneous network edge environments must consider devices with various constraints on computation power. Inspired by the advanced features of Digital Twins (DT), an emerging concept that mirrors the lifespan and operational characteristics of physical objects, we propose a novel Miner-Twins (MinT) architecture to enable a fair PoW consensus mechanism for blockchains in IoT environments. MinT adopts an edge-fog-cloud hierarchy. All physical miners of the blockchain are deployed as microservices on distributed edge devices, while fog/cloud servers maintain digital twins that periodically update miners’ running status. By timely monitoring miner’s footage that is mirrored by twins, a lightweight Singular Spectrum Analysis (SSA) based detection achieves to identify individual misbehaved miners that violate fair mining. Moreover, we also design a novel Proof-of-Behavior (PoB) consensus algorithm to detect byzantine miners that collude to compromise a fair mining network. A preliminary study is conducted on a proof-of-concept prototype implementation, and experimental evaluation shows the feasibility and effectiveness of proposed MinT scheme under a distributed byzantine network environment.

show abstract

Digital Twin for 5G and Beyond

Cited by 269 publications

References 10 publications

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

Semi-Supervised Support Vector Machine for Digital Twins Based Brain Image Fusion

Dissecting the Impact of Information and Communication Technologies on Digital Twins as a Service

Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)

Contact Info

Product

Resources

About