MRI: Development of Reconfigurable Environmental Intelligence Platform

该项目旨在开发可重构环境智能平台(REIP)，旨在缓解遥感的许多复杂方面，包括传感器节点设计、软件堆栈实施、隐私问题、带宽和集中计算限制，从而将启动时间从几年减少到几周。REIP将是一个即插即用的遥感基础设施，具有先进的边缘处理能力，可用于现场洞察生成。传感器网络动态地扩展了我们监控和研究世界的能力传感器网络已经为许多应用部署了专门的传感器网络，包括监控行人交通和户外噪声监测，随着传感器网络的使用案例的扩展和变得更加复杂，对传感器网络的需求不断增加。传感器不再简单地记录数据，它们在将数据发送到中央服务器之前，将其处理并转换为有用的东西。REIP的核心是一组硬件模块，这些模块连接在一起形成传感解决方案。每个感测模块将有许多变体，允许用户在复杂性/成本和功率/功能之间找到适当的折衷。REIP基础设施将扩大视听传感架构的使用范围，使其超越高度专业化的研究小组，这些研究小组能够设计、制造和购买所有必要的组件，并将其作为研究基础设施提供给更广泛的社区。REIP将在真实世界的应用程序上进行测试，包括观测、集成传感交通网络和室内传感，以减少暖通空调(暖通空调)系统中的浪费。专家将能够定制每个应用程序域。这项工作由一组在传感器网络、机器学习、深度学习、可视化、数据分析、人类计算接口、工程和职业治疗方面具有专业知识的研究人员领导，将为各种项目做出贡献，并必将产生重大的更广泛的影响。Reip和这项研究将直接影响不同的学生群体，并促进科学、技术、工程和数学(STEM)教育。将为所有相关的研究生和本科生提供指导机会。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project, developing a Reconfigurable Environmental Intelligence Platform (REIP) aims to alleviate many complex aspects of remote sensing, including sensor node design, software stack implementation, privacy issues, bandwidth, and centralized compute limitations, bringing down start up times from years to weeks. REIP will be a plug-and-play remote sensing infrastructure with advanced edge processing capabilities for in situ- insight generation. Sensor networks have dynamically expanded our ability to monitor and study our world Sensor networks have already deployed specialized sensor networks for many applications, including monitoring pedestrian traffic and outdoor noise monitoring and the need for sensing networks keeps increasing as the use cases for sensor networks expands and becomes more complex. Sensors no longer simply record data, they process and transforms it into something useful before sending it to central servers. At the core of REIP is a set of hardware modules that connect together to form a sensing solution. Each sensing module will come in a number of variants allowing the user to find the proper tradeoff between complexity/ ost and power/features. The REIP infrastructure will expand the use of audio-visual sensing architectures beyond the highly specialized research groups that are able to design, build, and purchase all the necessary components and make it available to a wider community as a research infrastructure. REIP will be tested on real-world applications, including observation, integrated sensing transportation networks, and indoor sensing for reducing waste in HVAC (Heating, Ventilating, and Air Conditioning) systems. Experts will be able to customize each application domain. Led by a team of researchers with expertise in sensor networks, machine learning, deep learning, visualization, data analysis, human computing interface, engineering, and occupational therapy, this work will contribute to a variety of projects and is bound to have significant broader impacts. REIP and this research will directly impact a diverse population of students and foster education in science, technology, engineering, and math (STEM). Mentoring opportunities will be provided for all the involved graduate and undergraduate studentsThis 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.