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Ella Peltonen
Researcher at University of Oulu
Publications - 41
Citations - 414
Ella Peltonen is an academic researcher from University of Oulu. The author has contributed to research in topics: Computer science & Edge computing. The author has an hindex of 9, co-authored 34 publications receiving 243 citations. Previous affiliations of Ella Peltonen include University of Helsinki & University College Cork.
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Proceedings ArticleDOI
Energy modeling of system settings: A crowdsourced approach
TL;DR: The results indicate that the models captured by the approach are both in line with previous studies on battery consumption and empirical measurements, providing a cost-effective way to construct energy models during normal operations of the device.
Posted Content
6G White Paper on Edge Intelligence
Ella Peltonen,Mehdi Bennis,Michele Capobianco,Merouane Debbah,Aaron Yi Ding,Felipe Gil-Castineira,Marko Jurmu,Teemu Karvonen,Markus Kelanti,Adrian Kliks,Teemu Leppänen,Lauri Lovén,Tommi Mikkonen,Ashwin Rao,Sumudu Samarakoon,Kari Seppänen,Pawel Sroka,Sasu Tarkoma,Tingting Yang +18 more
TL;DR: In this paper, the authors provide a vision for 6G Edge Intelligence and present edge computing along with other 6G enablers as a key component to establish the future intelligent Internet technologies as shown in this series of 6G White Papers.
Proceedings ArticleDOI
The hidden image of mobile apps: geographic, demographic, and cultural factors in mobile usage
Ella Peltonen,Eemil Lagerspetz,Jonatan Hamberg,Abhinav Mehrotra,Mirco Musolesi,Petteri Nurmi,Sasu Tarkoma +6 more
TL;DR: A large-scale analysis of geographic, cultural, and demographic factors in mobile usage reveals significant differences in app category usage across countries and it is shown that these differences, to large degree, reflect geographic boundaries.
Journal ArticleDOI
Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study
Kennedy Opoku Asare,Yannik Terhorst,Julio Vega,Ella Peltonen,Eemil Lagerspetz,Denzil Ferreira +5 more
TL;DR: In this paper, the authors explored the relationship between the behavioral features and depression using correlation and bivariate linear mixed models (LMMs) and leveraged 5 supervised machine learning (ML) algorithms with hyperparameter optimization, nested cross-validation, and imbalanced data handling to predict depression.
Journal ArticleDOI
Roadmap for edge AI
Aaron Yi Ding,Ella Peltonen,Tobias Meuser,Atakan Aral,Christian Becker,Schahram Dustdar,Thomas Hießl,Dieter Kranzlmüller,Madhusanka Liyanage,Setareh Maghsudi,Nitinder Mohan,Jörg Ott,Jan S. Rellermeyer,D. Schulte,Henning Schulzrinne,Gurkan Solmaz,Sasu Tarkoma,Blesson Varghese,Lars Wolf +18 more
TL;DR: Edge AI is envisioned to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimisation, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets.