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DeepMetaIoT: A Multimodal Deep Learning Framework Harnessing Metadata for IoT Sensor Data Classification
Journal article

DeepMetaIoT: A Multimodal Deep Learning Framework Harnessing Metadata for IoT Sensor Data Classification

Muhammad Sakib Khan Inan, Kewen Liao, Haifeng Shen, Prem Prakash Jayaraman, Federico Montori and Dimitrios Georgakopoulos
IEEE internet of things journal, Vol.12(20), pp.42352-42363
15/10/2025
DOI: https://doi.org/10.1109/JIOT.2025.3595556
Appears in  Recent Faculty of Science and Engineering Publications

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Abstract

IoT sensor data sensor metadata sensor time series deep learning multimodal fusion classification
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.

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