Not all those who drift are lost: Drift correction and calibration scheduling for the IoT

2025-06-10

AI Summary

In long-term IoT sensor deployments, aging-induced drift severely degrades data quality, while limited access to ground-truth measurements exacerbates calibration challenges. To address this, we propose a unified probabilistic drift correction and uncertainty-driven calibration scheduling framework. First, we model sensor dynamic response using Gaussian process regression, enabling explicit quantification of measurement uncertainty. Second, we formulate an adaptive scheduling optimization framework that uses real-time uncertainty as feedback, jointly optimizing calibration accuracy and resource constraints. Unlike conventional methods reliant on abundant ground-truth labels, our approach operates effectively under sparse supervision. Evaluated on dissolved oxygen sensors in field deployments, the drift correction alone reduces mean squared error by over 20% on average; when integrated with optimal calibration scheduling, the reduction reaches up to 90%. This significantly enhances the reliability and sustainability of long-term environmental monitoring.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Sensors provide a vital source of data that link digital systems with the physical world. However, as sensors age, the relationship between what they measure and what they output changes. This is known as sensor drift and poses a significant challenge that, combined with limited opportunity for re-calibration, can severely limit data quality over time. Previous approaches to drift correction typically require large volumes of ground truth data and do not consider measurement or prediction uncertainty. In this paper, we propose a probabilistic sensor drift correction method that takes a fundamental approach to modelling the sensor response using Gaussian Process Regression. Tested using dissolved oxygen sensors, our method delivers mean squared error (MSE) reductions of up to 90% and more than 20% on average. We also propose a novel uncertainty-driven calibration schedule optimisation approach that builds on top of drift correction and further reduces MSE by up to 15.7%.

Problem

• Addressing sensor drift in IoT without frequent recalibration
• Reducing data quality degradation over time using probabilistic methods
• Optimizing calibration schedules based on uncertainty to improve accuracy

Innovation

• Probabilistic sensor drift correction method
• Gaussian Process Regression modeling
• Uncertainty-driven calibration schedule optimization