DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911080146534400 |
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| author | Inan, Muhammad Sakib Khan Liao, Kewen Shen, Haifeng Jayaraman, Prem Prakash Georgakopoulos, Dimitrios Tang, Ming Jian |
| author_facet | Inan, Muhammad Sakib Khan Liao, Kewen Shen, Haifeng Jayaraman, Prem Prakash Georgakopoulos, Dimitrios Tang, Ming Jian |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_19996 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data Inan, Muhammad Sakib Khan Liao, Kewen Shen, Haifeng Jayaraman, Prem Prakash Georgakopoulos, Dimitrios Tang, Ming Jian Machine Learning Signal Processing 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 |
| title | DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2403.19996 |