DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data
Abstract
Internet of Things (IoT) sensor data or readings evince variations in timestamp range, sampling frequency, geographical location, unit of measurement, etc. Such presented sequence data heterogeneity makes it difficult for traditional time series classification algorithms to perform well. Therefore, addressing the heterogeneity challenge demands learning not only the sub-patterns (local features) but also the overall pattern (global feature). To address the challenge of classifying heterogeneous IoT sensor data (e.g., categorizing sensor data types like temperature and humidity), we propose a novel deep learning model that incorporates both Convolutional Neural Network and Bi-directional Gated Recurrent Unit to learn local and global features respectively, in an end-to-end manner. Through rigorous experimentation on heterogeneous IoT sensor datasets, we validate the effectiveness of our proposed model, which outperforms recent state-of-the-art classification methods as well as several machine learning and deep learning baselines. In particular, the model achieves an average absolute improvement of 3.37% in Accuracy and 2.85% in F1-Score across datasets
Cite
@article{arxiv.2403.19996,
title = {DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data},
author = {Muhammad Sakib Khan Inan and Kewen Liao and Haifeng Shen and Prem Prakash Jayaraman and Dimitrios Georgakopoulos and Ming Jian Tang},
journal= {arXiv preprint arXiv:2403.19996},
year = {2025}
}
Comments
Accepted for Publication and Presented in EAI MobiQuitous 2023 - 20th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services