In an era of real-time data and rapidly evolving urban dynamics, concept drift is a silent killer of AI accuracy. As patterns shift (think lockdowns, weather anomalies, or energy crises), traditional models fail.
As part of the CLEVER Project, we introduce a resilient, containerized edge computing architecture (EMDC) that automatically detects and adapts to concept drift in streaming data — all in real time, directly at the edge.
🚀 Why it matters:
- Smart buildings & cities constantly generate fluctuating sensor data
- Concept drift leads to outdated and inaccurate ML predictions
- Our platform runs distributed LSTM forecasting + drift detection using PHT, ADWIN, and KSWIN — all inside Kubernetes-managed Edge Micro Data Centres
📉 Results from Oulu Smart Campus:
- MAPE reduced from 8.5% ➡️ 3.88% after enabling concept drift detection
- Real-time CO₂ forecasting enhanced using Kafka, Apache Spark, and distributed LSTM
- Workloads are dynamically migrated across the edge-cloud continuum for efficiency and resilience
📊 Visual Insight:
Here’s a snapshot from the live deployment — mapping CO₂ levels across different zones of the smart campus (Tellus Arena). Our edge platform processes this data in real time to ensure AI stays accurate and responsive 👇
🔗 Discover how CLEVER is redefining smart infrastructure AI: https://www.cleverproject.eu