Learning to Deal with Sensor Drift

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Assessing Machine Learning Approaches to Address IoT Sensor Drift

We study and test several approaches from the literature with regard to their ability to cope with and adapt to sensor drift under realistic conditions.

The testing was performed on a publicly available gas sensor dataset exhibiting drift over time.

The results show substantial drops in sensing performance due to sensor drift in spite of the approaches.

We then discuss several issues identified with current approaches and outline directions for future research to tackle them.

Authors

Haining Zheng, Antonio Paiva

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