Edge intelligence for distributed acoustic sensing with ultracompact neural networks

Abstract

Distributed acoustic sensing (DAS) transforms optical fibres into dense arrays of vibration sensors for continuous, long-range monitoring with high spatial resolution. Yet converting these large-scale measurements into actionable information with compact models that retain task performance remains challenging. Existing artificial-intelligence approaches are typically optimized for specific sensing environments and tasks, but their computational requirements can hinder deployment in compact, power-constrained field systems. Here, we introduce DAS-NanoNet, a framework that automatically constructs lightweight neural networks for different DAS scenarios and tasks, together with OmniDAS, an open benchmark spanning six representative environments. Using lightweight temporal operators tailored to DAS signals, an adaptive neural architecture search strategy derives a compact network for each scenario-task pairing. Across classification, localization, compression, and denoising tasks in diverse DAS applications, DAS-NanoNet consistently yields highly compact architectures. On a six-class dataset, it achieves 99.68% accuracy with only 1,391 trainable parameters, while delivering comparable or better performance with up to 18,098-fold fewer parameters than previously reported high-accuracy networks. Its compact architecture further enables edge deployment, achieving sub 9 $\mu$s inference latency on the deployed hardware platform across different DAS applications. DAS-NanoNet provides a route towards edge intelligence for compact, low-power and real-time DAS systems through ultracompact networks tailored to individual sensing scenarios and tasks.

arXiv comment16 pages, 5 figures