Check the Challenges first! EU-IoT1, IoT Interfaces:Augmented Reality Interfaces based on the Smart Mirror concept

• MichMich MagicMirror, https://github.com/MichMich/MagicMirror
• https://www.makrmirror.io/

Far Edge:Sustainable IoT via TinyML

• https://www.tensorflow.org/lite
• https://pytorch.org/mobile/home/
• https://renode.io/
• https://antmicro.com/blog/2021/06/kenning-edge-ai-framework/
• https://antmicro.com/blog/2020/09/automatic-cpu-testing-with-embench/

Near Edge:Sustainable MEC applications

• https://try-mec.etsi.org/

Infrastructure: Simulating Time-sensitive and Deterministic networking IoT applications

• ns-3 simulator

  Data Spaces:Sustainable, user-centric smart mobility

• Open mobility data, https://openmobilitydata.org
• Crawdad datasets, https://crawdad.org/
• Microsoft geolife trajectories https://www.microsoft.com/en-us/download/details.aspx?id=52367
• OpenNebula https://docs.opennebula.io/6.0/quick_start/index.html
• Suggest yours!

European Factory Platform:IoT services for Manufacturing

• Sub-challenge EFPF1: Semantic matchmaking for the support of environmental monitoring of the shop-floor. You will be using the open-source EFPF TSMatch gateway to improve the existing apps, or to improve the interconnection between TSMatch and the EFPF Data spine.
• Sub-challenge EFPF2: Evaluate the EFPF SDK in developing digital smart manufacturing applications. You will be using the EFPF SDK studio to create applications.
• Sub-challenge EFPF3: Evaluate the usability of the Data Spine for the creation of composite applications. You will be testing the EFPF Data Spine and its usability, proposing specific improvements.
• Sub-challenge EFPF4: Analyse industrial data coming from EFPF partners and provide insights based on the EFPF analytics tools. You will be working with open-source Machine Learning Libraries and propose applications for the visualization and analysis of industrial data.