arXiv:2410.19742eess.SPcs.AI2024-10中稿 · ACM SenSys 2024被引 3

SALINA实现野外持续声呐实时分析,节能稳定运行6个月。

SALINA: Towards Sustainable Live Sonar Analytics in Wild Ecosystems

  • 动态适应时空变化,实时处理声呐数据
  • 精度提升9.5%,追踪性能提高10.1%
  • 适合长期野外部署的生态监测系统

声呐雷达利用声波反射获取水下物体与结构的视觉表征,对野生生态系统中的探索、测绘和持续监控至关重要。实时分析声呐数据对环境异常检测和季节性渔业管理等时效性应用尤为关键。然而,缺乏相关数据集与预训练深度神经网络模型,加之野外环境资源受限,制约了实时声呐分析系统的有效部署与持续运行。本文提出SALINA——一个可持续的实时声呐分析系统,支持空间与时间自适应的声呐数据实时处理,并通过稳健的能源管理模块实现低功耗运行。该系统在加拿大不列颠哥伦比亚省两条内陆河流部署长达六个月,实现了全天候24/7水下监测,支撑渔业管理与野生动物恢复工作。实测表明,SALINA平均精度提升达9.5%,追踪指标改善10.1%;能源管理模块成功应对极端天气,避免断电,降低应急成本。这些结果为野外声学数据系统的长期部署提供了重要参考。

原文摘要 · Abstract (English)

Sonar radar captures visual representations of underwater objects and structures using sound wave reflections, making it essential for exploration, mapping, and continuous surveillance in wild ecosystems. Real-time analysis of sonar data is crucial for time-sensitive applications, including environmental anomaly detection and in-season fishery management, where rapid decision-making is needed. However, the lack of both relevant datasets and pre-trained DNN models, coupled with resource limitations in wild environments, hinders the effective deployment and continuous operation of live sonar analytics. We present SALINA, a sustainable live sonar analytics system designed to address these challenges. SALINA enables real-time processing of acoustic sonar data with spatial and temporal adaptations, and features energy-efficient operation through a robust energy management module. Deployed for six months at two inland rivers in British Columbia, Canada, SALINA provided continuous 24/7 underwater monitoring, supporting fishery stewardship and wildlife restoration efforts. Through extensive real-world testing, SALINA demonstrated an up to 9.5% improvement in average precision and a 10.1% increase in tracking metrics. The energy management module successfully handled extreme weather, preventing outages and reducing contingency costs. These results offer valuable insights for long-term deployment of acoustic data systems in the wild.

声呐分析实时系统生态监测节能部署

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