A new blog post has been published focusing on the Artificial Intelligence of Things (AIoT) and the outcomes of the AIoTwin project.
AIoT is revolutionizing the way humans and systems interact with the physical world by embedding intelligence directly into devices — from city cameras to industrial sensors. By integrating AI across Cloud-Edge-IoT (CEI) environments, AIoT enables systems that can learn, reason, and make autonomous decisions in real time.
However, this transition from cloud-only or edge-only AI to a full computing continuum introduces new challenges, including determining where AI models should run — in the cloud, at the edge, or across both.
The first blog post, authored by Ivana Podnar Zarko, explores several key aspects:
-
Real-time traffic monitoring as a practical AIoT use case
-
Cloud vs. edge-based AI: accuracy, latency, network load, and privacy trade-offs
-
Why hybrid deployments across the computing continuum are the future
-
Key technical challenges: optimal service placement, dynamic routing, federated learning, fault tolerance, and model updates
As a solution, the AIoTwin Orchestration Middleware has been introduced. Developed by researchers at IoTLab@FER (Ivan Čilić, Ana Petra Jukić, Katarina Vuknić) in collaboration with researchers from Technische Universität Wien (Pantelis Frangoudis, Anna Lackinger, Ilir Murturi, Alireza Furutanpey, Schahram Dustdar), the middleware delivers a suite of open-source tools — including the fl-orchestrator, Flower extension for HFL, and QEdgeProxy. These tools form the foundation for scalable, adaptive, and intelligent AIoT deployments spanning from the edge to the cloud.
The full blog post is available here, with additional technical articles from consortium members to follow.