Enhancing IoT sensors precision through sensor drift calibration with variational autoencoder
Author Identifier
Md Kamal Hossain: https://orcid.org/0009-0009-4032-0491
Iftekhar Ahmad: https://orcid.org/0000-0003-4441-9631
Daryoush Habibi: https://orcid.org/0000-0002-7662-6830
Muhammad Waqas: https://orcid.org/0000-0003-0814-7544
Document Type
Journal Article
Publication Title
IEEE Internet of Things Journal
Publisher
IEEE
School
School of Engineering
RAS ID
76654
Funders
Edith Cowan University
Abstract
IoT sensors are made of physical materials, and due to natural decay in materials, sensor data drifts over time. Even though sensors are calibrated after deploying at the site, the accumulation of errors in sensor measurements due to sensor drifts renders the data progressively irrelevant, creating significant issues for end applications. In this paper, we propose a software-driven drift detection and calibration framework based on probabilistic observation in latent space using Variational Autoencoders (VAEs). The proposed method utilizes the latent distribution of the generative model from sampled observational data, which are collected during the calibration phase of the deployed sensors. Variational inference in VAEs is employed to approximate the true posterior distribution for detecting sensor drifts, incorporating metrics such as Kullback-Leibler (KL) divergence. Additionally, reconstruction loss is utilized for calibrating the sensors. Both simulated and real-world sensor data are used to evaluate the proposed method. Experimental results demonstrate significant improvement over existing drift detection and calibration techniques.
DOI
10.1109/JIOT.2024.3503616
Access Rights
subscription content
Comments
Hossain, M. K., Ahmad, I., Habibi, D., & Waqas, M. (2024). Enhancing IoT sensors precision through sensor drift calibration with variational autoencoder. IEEE Internet of Things Journal. Advance online publication. https://doi.org/10.1109/JIOT.2024.3503616