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, Dimitrios Georgakopoulos

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 article, we propose DeepMetaIoT, a multimodal deep learning (DL) 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 pretrained 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 DL baselines.

External IDs:dblp:journals/iotj/InanLSJMG25