IoT-Hub: New IoT data-platform for virtual research environments

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper presents IoT-Hub a new scalable, elastic, efficient, and portable Internet of Things (IoT) data-platform based on microservices for monitoring and analysing largescale sensor data in real-time. IoT-Hub allows us to collect, process, and store large amounts of data from multiple sensors in distributed locations-which could be deployed as a backend for Virtual Research Environments (VRE) or Science Gateways. In the proposed data-platform, all required software, which involves a variety of state-of-the-art open-source middleware, is packed into containers and deployed in a cloud environment. As a result, the engineering and computational time and costs for deployment and execution is significantly reduced.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume2357
StatePublished - 2019
Event10th International Workshop on Science Gateways, IWSG 2018 - Edinburgh, United Kingdom
Duration: Jun 13 2018Jun 15 2018

Funding

Acknowledgments. This work was carried out when the lead author was with the British Geological Survey. It was funded under the Scottish Informatics and Computer Science Alliance with the Postdoctoral and Early Career Researcher Exchanges fellowship, partially funded by DOE under Contract DESC0012636, “ Panorama - Predictive Modeling and Diagnostic Monitoring of Extreme Science Workflows”, and by “ DARE -Delivering Agile Research Excellence on European e-Infrastructures”, EU H2020 funded project (No 777413). We thank the NSF Chameleon Cloud for providing time grants to access their resources. This work contains British Geological Survey materials NERC [2017 and 2018 years]. This work was carried out when the lead author was with the British Geological Survey. It was funded under the Scottish Informatics and Computer Science Alliance with the Postdoctoral and Early Career Researcher Exchanges fellowship, partially funded by DOE under Contract DESC0012636, " Panorama-Predictive Modeling and Diagnostic Monitoring of Extreme Science Workflows", and by " DARE-Delivering Agile Research Excellence on European e-Infrastructures", EU H2020 funded project (No 777413). We thank the NSF Chameleon Cloud for providing time grants to access their resources. This work contains British Geological Survey materials NERC [2017 and 2018 years].

Keywords

  • Containers
  • Data Science
  • Data-Frameworks
  • IoT
  • Microservices
  • Science Gateway
  • Virtual Research Environment

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