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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.19281 |
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| _version_ | 1866917049893126144 |
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| author | Lan, Xiyang Li, Xin Teng, Yinglei |
| author_facet | Lan, Xiyang Li, Xin Teng, Yinglei |
| contents | Phase-sensitive optical time-domain reflectometry Φ-OTDR has emerged as a promising sensing technology in Internet of Things (IoT) infrastructures, enabling large-scale distributed acoustic sensing (DAS) for real-time monitoring at the edge in smart cities, industrial pipelines, and critical infrastructures. However, accurately recognizing events from massive Φ-OTDR data streams remains challenging, as existing deep learning methods either disrupt the inherent spatiotemporal structure of signals or incur prohibitive computational costs, limiting their applicability in resource-constrained edge computing scenarios. To overcome these challenges, we propose a novel STFT-based Attention-Enhanced Convolutional Neural Network (STFT-AECNN), which represents multi-channel time-series data as stacked spectrograms to fully exploit their spatiotemporal characteristics while enabling efficient 2D CNN processing. A Spatial Efficient Attention Module (SEAM) is further introduced to adaptively emphasize the most informative channels, and a joint Cross-Entropy and Triplet loss is adopted to enhance the discriminability of the learned feature space. Extensive experiments on the public BJTU Φ-OTDR dataset demonstrate that STFT-AECNN achieves a peak accuracy of 99.94% while maintaining high computational efficiency. These results highlight its potential for real-time, scalable, and robust event recognition in edge-based DAS systems, paving the way for reliable and intelligent IoT sensing applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19281 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Attention-Enhanced Φ-OTDR Event Recognition Framework for Edge-Based Distributed Acoustic Sensing Lan, Xiyang Li, Xin Teng, Yinglei Signal Processing Phase-sensitive optical time-domain reflectometry Φ-OTDR has emerged as a promising sensing technology in Internet of Things (IoT) infrastructures, enabling large-scale distributed acoustic sensing (DAS) for real-time monitoring at the edge in smart cities, industrial pipelines, and critical infrastructures. However, accurately recognizing events from massive Φ-OTDR data streams remains challenging, as existing deep learning methods either disrupt the inherent spatiotemporal structure of signals or incur prohibitive computational costs, limiting their applicability in resource-constrained edge computing scenarios. To overcome these challenges, we propose a novel STFT-based Attention-Enhanced Convolutional Neural Network (STFT-AECNN), which represents multi-channel time-series data as stacked spectrograms to fully exploit their spatiotemporal characteristics while enabling efficient 2D CNN processing. A Spatial Efficient Attention Module (SEAM) is further introduced to adaptively emphasize the most informative channels, and a joint Cross-Entropy and Triplet loss is adopted to enhance the discriminability of the learned feature space. Extensive experiments on the public BJTU Φ-OTDR dataset demonstrate that STFT-AECNN achieves a peak accuracy of 99.94% while maintaining high computational efficiency. These results highlight its potential for real-time, scalable, and robust event recognition in edge-based DAS systems, paving the way for reliable and intelligent IoT sensing applications. |
| title | An Attention-Enhanced Φ-OTDR Event Recognition Framework for Edge-Based Distributed Acoustic Sensing |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.19281 |