Real-Time Machine Learning-Based IoT Data Analysis in the Edge Cloud Continuum Using AC<sup>3</sup>
The demo highlights how IoT data from Sensirion sensors and Raspberry Pis are processed using the Agile and Cognitive Cloud-edge Continuum Management (AC<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>) framework. This approach optimizes data processing locations dynamically, balancing edge response and cloud computational power, enhancing real-time building management and smart city applications.
Authors
- Nikolaos Tsironis (ORCID: https://orcid.org/0009-0009-8084-613X)
- Golshan Famitafreshi (ORCID: https://orcid.org/0000-0003-0634-0041)
- Shreya K. Chari
- João Fonseca (ORCID: https://orcid.org/0000-0003-4490-2746)
- Gereziher Adhane (ORCID: https://orcid.org/0000-0002-2616-5757)
- Kostas Ramantas (ORCID: https://orcid.org/0000-0002-1304-784X)
- J. Ojeda
- Dimitrios Amaxilatis (ORCID: https://orcid.org/0000-0001-9938-6211)
- Navideh Ghafouri (ORCID: https://orcid.org/0000-0003-2961-7146)
- Themistoklis Sarantakos (ORCID: https://orcid.org/0000-0002-7517-6997)
- Swastika Roy (ORCID: https://orcid.org/0000-0002-8895-6562)
- Christos Verikoukis
Institutions
- University of Patras (GR)
- Iquadrat (Spain) (ES)
Publication Details
- Published
- 2024-10-21
- DOI
- https://doi.org/10.1109/camad62243.2024.10942957
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.00