Experimental Evaluation of Long-Term Concept Drift and Its Mitigation in WiFi CSI Sensing

WiFi channel state information (CSI) and machine learning-based environmental sensing have gained significant attention. However, it is empirically known that the accuracy of trained models degrades over time. In this study, we experimentally demonstrate that this temporal degradation in CSI sensing model accuracy is caused by concept drift, a phenomenon where the distribution of CSI shifts over time. Specifically, we provide evidence of long-term concept drift based on CSI measurements taken in a static environment over the course of seven days. Furthermore, we show that by applying noise-based data augmentation during model training, the impact of long-term concept drift can be mitigated. This approach alleviates the need for continuous model fine-tuning, which is resource-intensive due to its reliance on costly labeled data.