Towards a Unified Architecture Powering Scalable Learning Models with IoT Data Streams, Blockchain, and Open Data

Author:

Debauche Olivier1234ORCID,Nkamla Penka Jean Bertin3ORCID,Hani Moad3ORCID,Guttadauria Adriano3ORCID,Ait Abdelouahid Rachida5ORCID,Gasmi Kaouther6ORCID,Ben Hardouz Ouafae5ORCID,Lebeau Frédéric27ORCID,Bindelle Jérôme28ORCID,Soyeurt Hélène24ORCID,Gengler Nicolas29ORCID,Manneback Pierre3ORCID,Benjelloun Mohammed3ORCID,Bertozzi Carlo1ORCID

Affiliation:

1. Elevéo, R&D Service, Innovation Department, Awé Group, 5590 Ciney, Belgium

2. Gembloux Agro-Bio Tech, Terra, University of Liège, 5030 Gembloux, Belgium

3. Faculty of Engineering, ILIA Unit, University of Mons, 7000 Mons, Belgium

4. Gembloux Agro-Bio Tech, Modeling and Development, University of Liège, 5030 Gembloux, Belgium

5. Faculty of Sciences Ben M’sik, Hassan II University—Casablanca, Casablanca P.O. Box 7955, Morocco

6. National Engineering School of Tunis, Tunis El Manar University, 1080 Tunis, Belgium

7. Gembloux Agro-Bio Tech, Digital Energy & Agriculture Lab, University of Liège, 5030 Gembloux, Belgium

8. Gembloux Agro-Bio Tech, Animal Production Engineering and Nutrition, University of Liège, 5030 Gembloux, Belgium

9. Gembloux Agro-Bio Tech, Animal Production and Nutrition Engineering, University of Liège, 5030 Gembloux, Belgium

Abstract

The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms.

Publisher

MDPI AG

Subject

Information Systems

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3