IncQuery Labs presenting at the Eclipse SAM IoT 2020 conference

October 9, 2020

 IncQuery Labs presenting at the Eclipse SAM IoT 2020 conference

The Eclipse virtual conference on Security, Artificial Intelligence, and Modeling for the next-generation Internet of Things (Eclipse SAM IoT 2020) was held between 17-18 September. The aim of the event was to bring together a diverse community of both  industry practitioners and academy researchers to discuss the latest innovations  around, e.g., edge computing, AI-based analytics, digital twins, and new security and trust schemes for the next-generation industrial Internet of Things (IIoT). The concept and realization of numerous fields, such as smart cities, smart factories, critical infrastructures and cooperative services might benefit from adopting these technologies.  

Researchers of this field were invited to present their work at the virtual conference. We are proud to say that one of them was our colleague, Géza Kulcsár PhD, who presented our paper with the title Bringing Clouds Down to Earth: Modeling Arrowhead Deployments via Eclipse Vorto.

This research paper was born from an ongoing collaboration with the R&D team from Bosch.IO, Germany. Géza presented an Eclipse-based integration approach combining our modeling engine, the Eclipse Arrowhead IoT orchestration solution, and Eclipse Vorto, a cloud repository for digital device twins. Researchers will widen the tooling horizon of IoT by involving the CATIA No Magic modeling platform for design and integration tasks.

The long-term goal of this collaboration is a multi-domain, multi-platform IIoT suite, encompassing every engineering phase of a complex IoT deployment, from model-based functional design to IIoT edge devices and their digital twins.

Watch the presentation here (edited):

Any dissemination of results must indicate that it reflects only the author's view and that the JU is not responsible for any use that may be made of the information it contains.
Project no. 2019-2.1.3-NEMZ_ECSEL-2019-00003 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the 2019-2.1.3-NEMZ_ECSEL funding scheme.
This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No. 826452. The JU receives support from the European Union's Horizon 2020 research and innovation programme and Sweden, Austria, Poland, Germany, Italy, Czech Rebuplic, Netherlands, Belgium, Latvia, Romania, France, Hungary, Portugal, Finland, Turkey, Norways, Switzerland.