{"ID":2867310,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19281","arxiv_id":"2509.19281","title":"An Attention-Enhanced Φ-OTDR Event Recognition Framework for Edge-Based Distributed Acoustic Sensing","abstract":"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.","short_abstract":"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, a...","url_abs":"https://arxiv.org/abs/2509.19281","url_pdf":"https://arxiv.org/pdf/2509.19281v2","authors":"[\"Xiyang Lan\",\"Xin Li\",\"Yinglei Teng\"]","published":"2025-09-23T17:48:40Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}