# Assessing Machine Learning Approaches to Address IoT Sensor Drift

arXiv: https://arxiv.org/abs/2109.04356 · HTML: https://mlsanity.com/paper/2109.04356/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Confidence Regularized Self-Training](https://mlsanity.com/paper/1908.09822/) (2019)
- [Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data](https://mlsanity.com/paper/2010.03622/) (2020)
- [Understanding Self-Training for Gradual Domain Adaptation](https://mlsanity.com/paper/2002.11361/) (2020)
- [An introduction to domain adaptation and transfer learning](https://mlsanity.com/paper/1812.11806/) (2018)
- [A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams](https://mlsanity.com/paper/2104.10529/) (2021)
- [Fault Detection and Identification using Bayesian Recurrent Neural Networks](https://mlsanity.com/paper/1911.04386/) (2019)
- [DEEVA: A Deep Learning and IoT Based Computer Vision System to Address Safety and Security of Production Sites in Energy Industry](https://mlsanity.com/paper/2003.01196/) (2020)
- [Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach](https://mlsanity.com/paper/1901.02321/) (2018)
- [Advancing from Predictive Maintenance to Intelligent Maintenance with AI and IIoT](https://mlsanity.com/paper/2009.00351/) (2020)

## Abstract (arXiv)

The proliferation of IoT sensors and their deployment in various industries and applications has brought about numerous analysis opportunities in this Big Data era. However, drift of those sensor measurements poses major challenges to automate data analysis and the ability to effectively train and deploy models on a continuous basis. In this paper 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. Most of these approaches are recent and thus are representative of the current state-of-the-art. 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.