Collaborative Research: CPS: Medium: A3EM: Animal-borne Adaptive Acoustic Environmental Monitoring

合作研究：CPS：媒介：A3EM：动物源性自适应声环境监测

• 批准号: 2312391
• 负责人: Akos Ledeczi
• 金额: $ 75万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312391&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; A3EM

The application of acoustic monitoring in ecological sciences has grown exponentially in the last two decades. It has been used to answer many questions, including detecting the presence or absence of animal species in an environment, evaluating animal behavior, and identifying ecological stressors and illegal activities. However, current uses are limited to the coverage of relatively small geographic areas with a fixed number of sensors. Animal-borne GPS-based location trackers paired with other sensors are another widely used tool in aiding wildlife conservation and ecosystem monitoring. Since capturing and collaring wild animals is a traumatic event for them, as well as being expensive and resource-intensive, multiyear deployments are required. There are severely limited opportunities to recharge batteries making relatively power-hungry sensing, such as acoustic monitoring, out of reach for existing tracking collars. The aim of the A3EM project is to devise an animal-borne adaptive acoustic monitoring system to enable long-term, real-time observation of the environment and behavior of wildlife. Animal-borne acoustic monitoring will be a novel tool that may provide new insights into biodiversity loss, a severe but underappreciated problem of our time. Combining acoustic monitoring with location tracking collars will enable entirely new applications that will facilitate census gathering and monitoring of threatened and endangered species, detecting poachers of elephants in Africa or caribou in Alaska, and evaluating the effects of mining and logging on wildlife, among many others. All data, hardware designs, and software source code will be released to the public domain, enabling tracking collar manufacturers to include the technology within their products.A3EM constitutes a complex cyber-physical architecture involving humans, animals, distributed sensing devices, intelligent environmental monitoring agents, and limited power and network connectivity. This intermittently connected CPS, with a power budget an order of magnitude lower than typical, calls for novel approaches with a high level of autonomy and adaptation to the physical environment. A3EM will employ a unique combination of supervised and semi-supervised embedded machine learning to identify new and unexplored event classes in a given environment, dynamically control and adjust parameters related to data acquisition and storage, opportunistically share knowledge and data between distributed sensing devices, and optimize the management of storage and communication to minimize resource needs. These methods will be evaluated through the creation of a wearable acoustic monitoring system used to support ecological applications such as enhanced wildlife protection, rare species identification, and human impact studies on animal behavior.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

声学监测在生态科学中的应用在过去的二十年中呈指数级增长。它已被用于回答许多问题，包括检测环境中动物物种的存在或不存在，评估动物行为，识别生态压力源和非法活动。然而，目前的使用仅限于用固定数量的传感器覆盖相对较小的地理区域。动物携带的基于GPS的定位跟踪器与其他传感器配对是另一种广泛使用的工具，用于帮助野生动物保护和生态系统监测。由于捕获和捕捉野生动物对他们来说是一个创伤性事件，而且费用昂贵，资源密集，因此需要多年的部署。对电池进行再充电的机会非常有限，使得相对耗电的感测（例如声学监测）对于现有的跟踪项圈来说是遥不可及的。A3EM项目的目的是设计一种动物传播的自适应声学监测系统，以便对野生动物的环境和行为进行长期实时观察。动物传播的声学监测将是一种新的工具，可能会提供新的见解生物多样性丧失，我们这个时代的一个严重的，但未得到重视的问题。将声学监测与位置跟踪项圈相结合将实现全新的应用，这将有助于普查收集和监测受威胁和濒危物种，检测非洲大象或阿拉斯加驯鹿的偷猎者，以及评估采矿和伐木对野生动物的影响等。所有数据、硬件设计和软件源代码都将发布到公共领域，使追踪项圈制造商能够在其产品中包含该技术。A3EM构成了一个复杂的网络物理架构，涉及人类、动物、分布式传感设备、智能环境监测代理以及有限的电力和网络连接。这种间歇性连接的CPS的功率预算比典型的低一个数量级，需要具有高度自主性和适应物理环境的新方法。A3EM将采用监督和半监督嵌入式机器学习的独特组合，以识别给定环境中新的和未探索的事件类别，动态控制和调整与数据采集和存储相关的参数，在分布式传感设备之间适时共享知识和数据，并优化存储和通信管理，以最大限度地减少资源需求。这些方法将通过创建可穿戴声学监测系统进行评估，该系统用于支持生态应用，如加强野生动物保护，稀有物种识别和人类对动物行为的影响研究。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。