Roadmap for Edge AI: A Dagstuhl Perspective

no code implementations • 27 Nov 2021 • Aaron Yi Ding, Ella Peltonen, Tobias Meuser, Atakan Aral, Christian Becker, Schahram Dustdar, Thomas Hiessl, Dieter Kranzlmuller, Madhusanka Liyanage, Setareh Magshudi, Nitinder Mohan, Joerg Ott, Jan S. Rellermeyer, Stefan Schulte, Henning Schulzrinne, Gurkan Solmaz, Sasu Tarkoma, Blesson Varghese, Lars Wolf

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI.

Edge-computing

Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures

no code implementations • 22 Nov 2021 • Hao Zhou, Atakan Aral, Ivona Brandic, Melike Erol-Kantarci

Microgrids (MGs) are important players for the future transactive energy systems where a number of intelligent Internet of Things (IoT) devices interact for energy management in the smart grid.

energy management Management +3