Hossain, Kamal, Ahmad, Iftekhar, Habibi, Dayoush and Waqas, Muhammad ORCID: https://orcid.org/0000-0003-0814-7544 (2024) Enhancing IoT sensors precision through sensor drift calibration with variational autoencoder. IEEE Internet of Things Journal. ISSN 2327-4662 (Online) (doi:10.1109/JIOT.2024.3503616)

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PDF (Accepted Conference Paper) 49196 WAQAS_Enhancing_IoT_Sensors_Precision_Through_Sensor_Drift_Calibration_With_Variational_Autoencoder_(AAM)_2024.pdf - Accepted Version Download (14MB) | Preview

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.

Item Type:	Article
Uncontrolled Keywords:	sensors, calibration, Internet of Things, servers, sensor systems, noise, data models, sensor phenomena and characterization, intelligent sensors, concept drift, sensor drift, variational autoencoder, soft calibration, latent distribution, Kullback-Leibler divergence
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	08 Jan 2025 11:30
URI:	http://gala.gre.ac.uk/id/eprint/49196

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