Digital Twin Driven Blockchain Based Reliable and Efficient 6G Edge Network

Ozdogan, Mehmet Ozgen; Çarkacioğlu, Levent; Canberk, Berk

doi:10.1109/dcoss54816.2022.00062

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…Due to the requirements of specialized processors that are able to process massive amounts of structured and unstructured data from different sources, DT has become a new approach for testing and assurance for AI workloads. A DT can be defined as a virtual copy of the physical asset of interest that provides an emulated software replica that enables continuous prototyping, testing, and optimization [89]. The visualization module of a DT is responsible for delivering data insights to end users, simulation, and intelligent operations, while the dashboards and commands rely on the visualization module for correct functionality.…”

Section: Looking Beyond Network Connectivity-the Confluencementioning

confidence: 99%

From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs

Hlophe,

Maharaj

2023

Applied Sciences

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As a result of the new telecommunication ecosystem landscape, wireless communication has become an interdisciplinary field whose future is shaped by several interacting dimensions. These interacting dimensions, which form the cyber–physical convergence, closely link the technological perspective to its social, economic, and cognitive sciences counterparts. Beyond the current operational framework of the Internet of Things (IoT), network devices will be equipped with capabilities for learning, thinking, and understanding so that they can autonomously make decisions and take appropriate actions. Through this autonomous operation, wireless networking will be ushered into a paradigm that is primarily inspired by the efficient and effective use of (i) AI strategies, (ii) big data analytics, as well as (iii) cognition. This is the Cognitive Internet of People Processes Data and Things (CIoPPD&T), which can be defined in terms of the cyber–physical convergence. In this article, through the discussion of how the cyber–physical convergence and the interacting dynamics of the socio-technical ecosystem are enablers of digital twins (DTs), the network DT (NDT) is discussed in the context of 6G networks. Then, the design and realization of edge computing-based NDTs are discussed, which culminate with the vehicle-to-edge (V2E) use cases.

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Section: Looking Beyond Network Connectivity-the Confluencementioning

confidence: 99%

From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs

Hlophe,

Maharaj

2023

Applied Sciences

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Blockchain for Edge Association in Digital Twin Empowered 6G Networks

Fancy,

Anand,

Sheeba

2024

Artificial Intelligence‐Enabled Blockchain Technology and Digital Twin for Smart Hospitals

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Adaptive secure malware efficient machine learning algorithm for healthcare data

Mohammed

Lakhan

Zebari

et al. 2023

CAAI Trans on Intel Tech

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Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices. On the other hand, IoT‐based heartbeat digital healthcare applications have been steadily increasing in popularity. These applications make a lot of data, which they send to the fog cloud to be processed further. In healthcare networks, it is critical to examine healthcare data for malicious intent. The malware is a peace code with polymorphic and metamorphic attack forms. Existing malware analysis techniques did not find malware in the content‐aware heartbeat data. The Adaptive Malware Analysis Dynamic Machine Learning (AMDML) algorithm for content‐aware heartbeat data in fog cloud computing is described in this article. Based on heartbeat data from health records, an adaptive method can train both pre‐ and post‐train malware models. AMDML is based on a rule called ‘federated learning,’ which says that malware analysis models are made at both the local fog node and the remote cloud to meet the performance workload safely. The simulation results show that AMDML outperforms machine learning malware analysis models in terms of accuracy by 60%, delay by 50%, and detection of original heartbeat data by 66% compared to existing malware analysis schemes.

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Digital Twin Driven Blockchain Based Reliable and Efficient 6G Edge Network

Cited by 9 publications

References 24 publications

From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs

From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs

Blockchain for Edge Association in Digital Twin Empowered 6G Networks

Adaptive secure malware efficient machine learning algorithm for healthcare data

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