Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
A digital twin (DT) is a digital representation of a physical asset that serves as its counterpart — or twin. DTs differ from static, three-dimensional models in that they are continuously updated with data from numerous sources. In one continually changing world of pervasive computing, where computational and human intelligence are expanding everywhere, DTs can be regarded as the backbone for addressing the synergy of software, devices, movable objects, networks, and people. In this paper, we present a novel perspective for designing, prototyping and testing pervasive and connected DTs for edge computing enabled industrial applications. The provided paradigm allows for the creation of computational models for cloud computing as well as the transmission of data and computational intelligence through analytic platforms. A case study is presented to demonstrate the possibilities of the suggested framework. According to the outlined findings, the proposed architecture contributes to effective maintenance and management of infrastructures and facilities.
Recommended Citation
Sanfilippo, Filippo; Langås, Even Falkenberg; Bukhari, Halima; and Robstad, Stian, "Pervasive and Connected Digital Twins for Edge Computing Enabled Industrial Applications" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 2.
https://aisel.aisnet.org/hicss-56/st/edge_computing/2
Pervasive and Connected Digital Twins for Edge Computing Enabled Industrial Applications
Online
A digital twin (DT) is a digital representation of a physical asset that serves as its counterpart — or twin. DTs differ from static, three-dimensional models in that they are continuously updated with data from numerous sources. In one continually changing world of pervasive computing, where computational and human intelligence are expanding everywhere, DTs can be regarded as the backbone for addressing the synergy of software, devices, movable objects, networks, and people. In this paper, we present a novel perspective for designing, prototyping and testing pervasive and connected DTs for edge computing enabled industrial applications. The provided paradigm allows for the creation of computational models for cloud computing as well as the transmission of data and computational intelligence through analytic platforms. A case study is presented to demonstrate the possibilities of the suggested framework. According to the outlined findings, the proposed architecture contributes to effective maintenance and management of infrastructures and facilities.
https://aisel.aisnet.org/hicss-56/st/edge_computing/2