Zhang, Y., Chaudhuri, T., Liu, P., Wang, L., Wu, M., & Li, X. (2023). DP-IINK: A Framework for Drift Prediction With Inter- and Intranode Knowledge Transfer With Less Data. IEEE Sensors Journal, 23(6), 5892–5900. https://doi.org/10.1109/jsen.2023.3242981
A gas sensor provides a system with signals to monitor and respond to events which 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 with large quantity, it limits the maintaining cost of them, 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 the gas sensors of same type cannot be one-for-all way because they vary due to small manufacturing discrepancy. Third, drift models for a sensor could even be different only because the sensor is under different scenarios. In this paper, we propose a new framework for drift prediction with inter and intra node knowledge transfer (DP-IINK) for widely deployed low-cost gas sensors. Knowledge transfer is employed for inter- and intra-node way, as well as for continuous and discontinuous drift. Our evaluation shows that our proposed framework achieves a mean MAPE as low as 5.18% for drift prediction with less data.
This research / project is supported by the A*STAR - AME Programmatic Funds
Grant Reference no. : A20H6b0151