DP-IINK: A Framework for Drift Prediction With Inter- and Intranode Knowledge Transfer With Less Data

A gas sensor provides a system with signals to monitor and respond to events that matter. However, some widely deployed sensors behave with drift, leading to inaccurate measurement. The system could malfunction due to the drift. Even more, if the system is not robust enough, the mistaken measurement could trigger some serious consequences. A drift prediction framework is necessary to contain this malfunction. On the other hand, when sensors are deployed in large quantities, it limits their maintenance cost, including the calibration cost. Data-driven frameworks show good potential for cheap calibration. But there are some challenges. First, sensor drift keeps occurring. The drift models need to be retrained over time. Second, the drift models for gas sensors of the same type cannot be a one-for-all way because they vary due to small manufacturing discrepancies. Third, drift models for a sensor could even be different only because the sensor is under different scenarios. In this article, we propose a new framework for drift prediction with inter- and intranode knowledge transfer (DP-IINK) for widely deployed low-cost gas sensors. Knowledge transfer is employed for inter- and intranode ways, as well as for continuous and discontinuous drift. Our evaluation shows that our proposed framework achieves a mean absolute percentage error (MAPE) as low as 5.18% for drift prediction with less data.