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Fabrication-as-a-Service: A Web-Based Solution for STEM Education Using Internet of Things

Gianluca Cornetta, Abdellah Touhafi, Mohammed Amine Togou, Gabriel-Miro Muntean

Onderzoeksoutput: Articlepeer review

20 Citaten (Scopus)

Samenvatting

Recently, fabrication laboratories (Fab Labs) have been shown to have a great impact on learners' academic and personal progress. As a result, an increasing effort is being put to integrate Fab Labs into schools' curricula. Yet, owing to the high cost of setting up and maintaining Fab Labs as well as the lack of sufficient funding for most schools and universities, only a limited number of institutions can afford them. In this article, we propose a new concept called Fabrication-as-a-Service (FaaS) that uses Internet of Things to democratize access to Fab Labs via enabling a wide learning community to remotely access these computer-controlled tools and equipment over the Internet. It employs a two-tier architecture consisting of a hub, deployed in the cloud, and a network of distributed Fab Labs. Each Fab Lab interacts with the hub and other digital labs via a Fab Lab Gateway. This is to support scalability and high availability of fabrication services as well as ensure the system's security. FaaS also adopts an innovative master-slave approach that uses inexpensive external hardware to monitor and control the activity of expensive fabrication equipment. This article also describes the FaaS deployment in the context of the European Union Horizon 2020 NEWTON project. Multiple scenarios have been deployed to fully illustrate the benefits of the FaaS architecture and to assess the performance of its communication protocol stack.

Originele taal-2English
Artikelnummer8915690
Pagina's (van-tot)1519-1530
Aantal pagina's12
TijdschriftIEEE Internet of Things Journal
Volume7
Nummer van het tijdschrift2
DOI's
StatusPublished - feb. 2020

Bibliografische nota

Funding Information:
This work was supported in part by the NEWTON Project funded by the European Union through the Horizon 2020 Research and Innovation Programme under Grant 688503, and in part by the Science Foundation Ireland Research Centres Programme under Grant 12/RC/2289 (Insight Centre for Data Analytics) and Grant 16/SP/3804 (ENABLE).

Funding Information:
Manuscript received July 29, 2019; revised October 1, 2019 and October 31, 2019; accepted November 21, 2019. Date of publication November 27, 2019; date of current version February 11, 2020. This work was supported in part by the NEWTON Project funded by the European Union through the Horizon 2020 Research and Innovation Programme under Grant 688503, and in part by the Science Foundation Ireland Research Centres Programme under Grant 12/RC/2289 (Insight Centre for Data Analytics) and Grant 16/SP/3804 (ENABLE). (Corresponding author: Mohammed Amine Togou.) G. Cornetta is with the Department of Information Engineering (CEU), Universidad San Pablo-CEU, E28668 Madrid, Spain (e-mail: [email protected]).

Publisher Copyright:
© 2014 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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