Journal article
DeepMetaIoT: A Multimodal Deep Learning Framework Harnessing Metadata for IoT Sensor Data Classification
IEEE internet of things journal, Vol.First online
2025
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Abstract
Internet of Things (IoT) sensor data, which capture time series physical measurements such as temperature and humidity, often lack proper classification. This limits their effective understanding, integration, and reuse. While sensor metadata—textual descriptions of the measurements—is sometimes available, it is frequently incomplete or ambiguous. As a result, classification often depends solely on the time series data. Leveraging both time series sensor readings and textual metadata for automated and accurate classification remains a challenge due to the heterogeneity and inconsistency of these data sources. In this paper, we propose DeepMetaIoT, a multimodal deep learning framework that integrates time series and textual data for classification. DeepMetaIoT employs a cross-residual architecture comprising a time series encoder and a text encoder based on a pre-trained large language model, enabling effective fusion of both modalities. Experimental results on real-world IoT sensor datasets show that DeepMetaIoT consistently outperforms state-of-the-art machine learning and deep learning baselines.
Details
- Title
- DeepMetaIoT: A Multimodal Deep Learning Framework Harnessing Metadata for IoT Sensor Data Classification
- Creators
- Muhammad Sakib Khan Inan - Deakin UniversityKewen Liao - Deakin UniversityHaifeng Shen - Southern Cross UniversityPrem Prakash Jayaraman - Swinburne University of TechnologyFederico Montori - University of BolognaDimitrios Georgakopoulos - Swinburne University of Technology
- Publication Details
- IEEE internet of things journal, Vol.First online
- Publisher
- IEEE
- Grant note
- Grant Number: ARC Discovery Project DP220101420
- Identifiers
- 991013303128302368
- Copyright
- © 2025, IEEE.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article