Using fog computing/edge computing to leverage Digital Twin

Pushpa, J.; Kalyani, S

doi:10.1016/bs.adcom.2019.09.003

Cited by 14 publications

(3 citation statements)

References 2 publications

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“…In fact, the analytics engine is a crucial part of digital twins because it turns straightforward observations into useful business data. It frequently draws strength from model-based machine learning (Pushpa & Kalyani, 2020). A digital twin also has to have dashboards for real-time monitoring, modeling tools, and numerical simulations.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review on Digital Twins Technology: A New Frontier in Agriculture

Dawn,

Ghosh,

Ghosh

et al. 2023

AIA

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Farming is crucial for various aspects of daily life, including food, the economy, environment, culture, and community. It provides employment opportunities, generates income, and increases the export of agricultural products, particularly in rural areas. Sustainable farming practices promote soil health, biodiversity, and ecosystem services, and are essential in many parts of the world. Farming is deeply rooted in cultures and traditions and is a way of life for many communities, passed down from generation to generation. Without farming, we would not have the abundance and variety of food that we enjoy today. Advancements in technology, such as artificial intelligence, machine learning, and the Internet of Things, have greatly impacted agriculture by producing vast amounts of digital data on crops, soil, and weather conditions. However, managing and analyzing this data can be challenging for farmers, especially those in developing nations. To address this issue, affordable digital farming solutions, including open-source software platforms, sensor networks, and mobile apps, are being developed to help farmers optimize their resources, increase yields, and profits. Digital twin technology can play a crucial role in digital farming by providing farmers with a virtual replica of their physical farm. It is a digital depiction of a real-world asset, such a farm or a particular crop field, that gathers information from sensors, weather stations, and satellite pictures. This technology has arisen that has been hailed as revolutionary in a number of fields, including manufacturing machines, construction, agriculture, healthcare, and the automotive and aerospace industries. However, the technology is still in its early stages in agriculture, and it can be challenging to handle the interactions between different farming-related digital twin components. Additionally, digital twinning can require significant investment in technology and infrastructure, which may be a barrier for small-scale farmers.

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Section: Literature Reviewmentioning

confidence: 99%

A Review on Digital Twins Technology: A New Frontier in Agriculture

Dawn,

Ghosh,

Ghosh

et al. 2023

AIA

View full text Add to dashboard Cite

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“…Authors of [102,103] have presented a big data processing framework for industries and maintenance in a DT situation. Cloud computation is one of the platforms that can be used to process and analyze big data [104,105]. It is important to implement applicable AI-ML techniques or algorithms to make the DT models more intelligent.…”

Section: Figure 5 Dt Relationship With Iot Big Data and Ai-mlmentioning

confidence: 99%

Digital Twin Perspective of Fourth Industrial and Healthcare Revolution

2022

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Digital Twin (DT) is bringing revolution to our lives by a digital representation of the physical system. DT is the creation of the joint usage of various technologies like Cyber-Physical System (CPS), Internet of Things (IoT), Big Data, Edge Computing (EC), Artificial Intelligence (AI), and Machine Learning (ML), etc. DTs are established to optimize a wide range of applications of industry, healthcare, smart cities, smart homes, etc. It is still in its early development stages. This paper fills the gaps by combining the extensive information on technologies utilized in the creation of DT in industry and healthcare. The paper focuses on studying the characteristics of DT, communication technologies and tools utilized in the creation of DT models, reference models, standards, and the researcher's recent work in smart manufacturing and healthcare. Challenges and open issues that need attention are also discussed.

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“…Addressing scalability challenges necessitates the development of robust systems and infrastructure capable of efficiently handling the expanding volume of data generated with DTs. This entails harnessing high-performance computing capabilities such as cloud computing, fog computing and edge computing technologies [126,127], using efficient mechanisms for data storage and processing, and utilising scalable network architectures. These measures facilitate seamless integration and analysis of substantial data quantities, enabling DTs to meet the demands of expanding projects.…”

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confidence: 99%

Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions

et al. 2023

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This paper presents a comprehensive understanding of current digital twin (DT) implementations in the construction industry, along with providing an overview of technologies enabling the operation of DTs in the industry. To this end, 145 publications were identified using a systematic literature review. The results revealed eight key areas of DT implementation including (i) virtual design, (ii) project planning and management, (iii) asset management and maintenance, (iv) safety management, (v) energy efficiency and sustainability, (vi) quality control and management, (vii) supply chain management and logistics, and (viii) structural health monitoring. The findings demonstrate that DT technology has the capacity to revolutionise the construction industry across these areas, enabling optimised designs, improved collaboration, real-time monitoring, predictive maintenance, enhanced safety practices, energy performance optimisation, quality inspections, efficient supply chain management, and proactive maintenance. This study also identified several challenges that hinder the widespread implementation of DT in construction, including (i) data integration and interoperability, (ii) data accuracy and completeness, (iii) scalability and complexity, (iv) privacy and security, and (v) standards and governance. To address these challenges, this paper recommends prioritising standardised data formats, protocols, and APIs for seamless collaboration, exploring semantic data modelling and ontologies for data integration, implementing validation processes and robust data governance for accuracy and completeness, harnessing high-performance computing and advanced modelling techniques for scalability and complexity, establishing comprehensive data protection and access controls for privacy and security, and developing widely accepted standards and governance frameworks with industry-wide collaboration. By addressing these challenges, the construction industry can unlock the full potential of DT technology, thus enhancing safety, reliability, and efficiency in construction projects.

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Using fog computing/edge computing to leverage Digital Twin

Cited by 14 publications

References 2 publications

A Review on Digital Twins Technology: A New Frontier in Agriculture

A Review on Digital Twins Technology: A New Frontier in Agriculture

Digital Twin Perspective of Fourth Industrial and Healthcare Revolution

Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions

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