Automation of Network edge Infrastructure & Applications with Artificial Intelligence

Project Key Information

Project Status: finished

Start Date: June 2021

End Date: January 2024

Budget (total): 11214 K€

Effort: 81.66 PY

Project-ID: C2019/3-2

Project Coordinator

Name: Ali Balador

Company: Ericsson AB

Country: Sweden


Project Consortium

Ericsson AB (EAB), Sweden

Arctoslabs AB, Sweden

Chalmers University of Technology (CTH), Sweden

Enoc System AB, Sweden

Royal Institute of Technology, KTH (Kungliga Tekniska Högskolan), Sweden

Hopsworks, Sweden

Qamcom Research and Technology AB, Sweden

RI.SE Research Institutes of Sweden AB, Sweden

Systemair AB, Sweden

Univrses AB, Sweden

Delta Electronics, Sweden

Kings College London, United Kingdom

HAL Robotics, United Kingdom

Konica-Minolta, United Kingdom

Opel Automobile GmbH, Germany

Technical University Braunschweig, Germany

Fraunhofer IPT, Germany

Fraunhofer IST, Germany

IconPro GmbH, Germany


Digital transformation is ongoing in many areas of today’s society, which will impact many aspects of people’s lives via means such as smart cities, robotic, transportation, and next-generation industries. At the same time, the current centralized cloud infrastructure is not adequate to serve the transformation’s requirements. We believe that three technologies can come together to shape a new secure service and application platform; 5G, edge-centric compute & artificial intelligence. In this context, European industry has a good position in 5G networks, transportation and industrial applications, but need to strengthen the position in a secure cloud, data centre and artificial intelligence technologies to be at the front of the development.

The primary objective of the ANIARA project is to provide enablers and solutions for high-performance services deployed and operated at the network edge. To manage complexity, we need to take advantage of artificial intelligence to complement traditional optimisation algorithms. Currently, deep edge network nodes will be deployed at locations not prepared for the power requirements of edge-centric compute. To answer this, we need to analyse requirements and develop methods to minimize energy consumption.

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