Context-aware Drift Detection for Quality-aware Data-Driven Smart City Digital Twins
Luca Serfilippi, Armir Bujari, Antonio Corradi · 2025
As urban environments become more complex, Smart Cities will increasingly depend on the integration of Internet of Things (IoT) devices and Digital Twin (DT) technologies to enable real-time monitoring, simulation, and predictive analytics, to ultimately improve quality of life. Data-driven approaches play a crucial role in optimizing city operations, but their effectiveness is hampered by data drift, shifts in data distributions over time that can degrade model performance. Frequent model retraining is a common solution, but it can be ineffective or lead to high computational costs. Alternatively, drift-driven approaches are often vulnerable to false detections caused by noisy IoT data, hardware failures, or cyber threats. To address that problem, we propose a context-aware drift detection algorithm that takes advantage of local correlations between data sources to detect actual real drifts while avoiding false positives. Furthermore, we present a decentralized layered architecture for embedding drift detection within the Smart City ecosystem and evaluate our approach by using a real-world dataset. Our approach demonstrates the viability of context-aware distributed drift detection, enhancing the reliability and efficiency of ML-driven Smart City applications.