IoT meets distributed AI : deployment scenarios of Bonseyes AI applications on FIWARE

Moor, Lucien (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland) ; Bitter, Lukas (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland) ; De Prado, Miguel (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland) ; Pazos Escudero, Nuria (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland) ; Ouerhani, Nabil (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland)

Bonseyes is an Artificial Intelligence (AI) platform composed of a Data Marketplace, a Deep Learning Toolbox, and Developer Reference Platforms with the aim of facilitating tech and non-tech companies a rapid adoption of AI as an enabler for their business. Bonseyes provides methods and tools to speed up the development and deployment of AI solutions on low power Internet of Things (IoT) devices, embedded computing systems, and data centre servers. In this work, we address the deployment and the integration of Bonseyes AI applications in a wider enterprise application landscape involving different applications and services. We leverage the well-established IoT platform FIWARE to integrate the Bonseyes AI applications into an enterprise ecosystem. This paper presents two AI application deployment and integration scenarios using FIWARE. The first scenario addresses use cases where edge devices have enough compute power to run the AI applications and there is only need to transmit the results to the enterprise ecosystem. The second scenario copes with use cases where an edge device may delegate most of the computation to an external/cloud server. Further, we employ FIWARE IoT Agent generic enabler to manage all edge devices related to Bonseyes AI applications. Both scenarios have been validated on concrete use cases and demonstrators.


Mots-clés:
Type de conférence:
short paper
Faculté:
Ingénierie et Architecture
Ecole:
HE-Arc Ingénierie
Institut:
Aucun institut
Adresse bibliogr.:
London, United Kingdom, 29-31 October 2019
Date:
2019-10
London, United Kingdom
29-31 October 2019
Pagination:
2 p.
Publié dans:
Proceedings of 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC), 29-31 October 2019, London, United Kingdom
DOI:
ISBN:
978-1-7281-1025-7
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 Notice créée le 2020-03-17, modifiée le 2021-03-23

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