A Cloud-based Collaboration Platform for Model-based Design of Cyber-Physical Systems

Larsen, Peter Gorm; Macedo, Hugo Daniel; Fitzgerald, John S.; Pfeifer, Holger; Benedikt, Martin; Tonetta, Stefano; Marguglio, Angelo; Gusmeroli, Sergio; Suciu, George

doi:10.5220/0009892802630270

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…Indeed, the sustainability of DIHs and of networks of DIHs is still an unexplored theme 51 that needs to be addressed to allow to give continuity to the precious and strategic support action of DIHs to the digital transition of European companies. Digital platforms, as in the case of HUBCAP project, [52][53][54] can be a strategic asset in sustaining the economic sustainability of such networks, giving the possibility to directly propose to the customers the suitable assets depending on their needs and industries. For sure, the role of boundary organization of DIHs could be better investigated based on the results provided with this research.…”

Section: Discussionmentioning

confidence: 99%

The D-BEST Based digital innovation hub customer journey analysis method: Configuring DIHs unique value proposition

Sassanelli

Terzi

2022

International Journal of Engineering Business Management

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Digital Innovation Hubs (DIHs) are ecosystems bolstering European companies to overtake innovation hindrances and drive Europe to become the world leading innovator in the industry digital revolution. Each of such organizations can provide a certain list of services, that can be classified and grouped in five macro-classes according to the Data-driven Business-Ecosystem-Skills-Technology (D-BEST) reference model, able to decode DIHs’ service portfolio and to shape collaborative networks in the Industry 4.0 age. However, to support an easier codification of DIH support actions, which also directly entails the engagement of enterprises in the DIH ecosystems, a method able to analyze typical Customer Journeys (CJs) is needed. Therefore, this paper proposes the D-BEST based DIH CJ analysis method, able to configure DIHs’ unique value proposition, mapping on the five macro-classes of services of the D-BEST the digital transformation processes of the two main categories of DIH customers (technology end-users and technology providers). The method analyses the service provision process of single DIHs, evidencing their strengths and weaknesses, and is also effective in suggesting possible collaborations and joint service provision in a network of multiple DIHs, being able to unveil the commonalities and complementarities among the different journeys.

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Section: Discussionmentioning

confidence: 99%

The D-BEST Based digital innovation hub customer journey analysis method: Configuring DIHs unique value proposition

Sassanelli

Terzi

2022

International Journal of Engineering Business Management

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“…Finally, it is also worth noting that these other sectors are now turning to cloud-based solutions 81 to increase to accessibility and reach of simulation-based design and widespread low-cost adoption of FMI. FMI, co-simulation and cloud-based simulation services offer a potential next step in simulation modelling for rail decarbonisation.…”

Section: Discussion and Concluding Commentsmentioning

confidence: 99%

A feasibility assessment of multi-modelling approaches for rail decarbonisation systems simulation

Golightly

Pierce

Palacín

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part F:

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Simulation is an important tool to support rail decarbonisation but can be challenging due to heterogeneous models, simulation tools and skill sets, and concerns around intellectual property. Multi-modelling, a proven methodology in sectors such as aerospace and automotive, uses Functional Mock-up Interface (FMI) and co-simulation to potentially overcome these problems. This paper presents a feasibility study of multi-modelling for rail decarbonisation, using a combination of audit of current state of the art, technical implementation and stakeholder consultation. The audit showed that while current uptake of FMI in rail is low, there is potential to repurpose models from pre-existing tools and apply them within multi-modelling. The technical feasibility assessment demonstrated how multi-modelling could generate flexible simulation outputs to identify decarbonisation systems effects both for urban and mainline rail, including rapid integration of pre-existing MATLAB Simulink models. Work with industry stakeholders identified use cases where multi-modelling would benefit rail decarbonisation, as well as barriers and enablers to adoption. Overall, the study demonstrates the feasibility and considerations for multi-modelling to support rail decarbonisation efforts, and the future developments necessary for wider rollout.

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“…Create a unique web entry point for sending work to the ad hoc cloud system. This obtains more libraries and packages (i.e., Liferay portal project [ 66 ]). In addition, the system 'WS-PGRADE/gUse' with some non-required features, such as 'WS-PGRADE' and 'DCI-Bridge', can be used to submit work for future research on the ad hoc cloud system platform.…”

Section: Proposed Modelmentioning

confidence: 99%

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

et al. 2022

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In the early days of digital transformation, the automation, scalability, and availability of cloud computing made a big difference for business. Nonetheless, significant concerns have been raised regarding the security and privacy levels that cloud systems can provide, as enterprises have accelerated their cloud migration journeys in an effort to provide a remote working environment for their employees, primarily in light of the COVID-19 outbreak. The goal of this study is to come up with a way to improve steganography in ad hoc cloud systems by using deep learning. This research implementation is separated into two sections. In Phase 1, the “Ad-hoc Cloud System” idea and deployment plan were set up with the help of V-BOINC. In Phase 2, a modified form of steganography and deep learning were used to study the security of data transmission in ad-hoc cloud networks. In the majority of prior studies, attempts to employ deep learning models to augment or replace data-hiding systems did not achieve a high success rate. The implemented model inserts data images through colored images in the developed ad hoc cloud system. A systematic steganography model conceals from statistics lower message detection rates. Additionally, it may be necessary to incorporate small images beneath huge cover images. The implemented ad-hoc system outperformed Amazon AC2 in terms of performance, while the execution of the proposed deep steganography approach gave a high rate of evaluation for concealing both data and images when evaluated against several attacks in an ad-hoc cloud system environment.

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A Cloud-based Collaboration Platform for Model-based Design of Cyber-Physical Systems

Cited by 13 publications

References 13 publications

The D-BEST Based digital innovation hub customer journey analysis method: Configuring DIHs unique value proposition

The D-BEST Based digital innovation hub customer journey analysis method: Configuring DIHs unique value proposition

A feasibility assessment of multi-modelling approaches for rail decarbonisation systems simulation

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

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