I-Corps: Artificial Intelligence (AI)-Based Sensing and Data Efficient Sampling, Transmission, Storage, Analysis and Cloud Computing

I-Corps：基于人工智能（AI）的传感和数据高效采样、传输、存储、分析和云计算

• 批准号: 2235121
• 负责人: Soundar Kumara
• 金额: $ 5万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2235121&HistoricalAwards=false
• 关键词: Corps; Artificial; Intelligence; AI; Based

The broader impact/commercial potential of this I-Corps project is to improve data from sensors that have become ubiquitous in daily life, including personal devices, smart homes and infrastructure, industrial machines and factories, autonomous machines, and the natural environment. Sensor-based ‘big data’ offers real-time visibility into a variety of systems. However, the volume, speed, and variety of this data will soon overwhelm the ability to ingest, refine, and analyze all the data. This project seeks to develop modeling methodologies that will improve the way data is collected, processed, and analyzed in a variety of industries involving embedded systems such as Internet of Things (IoT), space exploration, and biotechnology. Remote monitoring of industrial machines and distributed sensing in the oceans and the biosphere are some application areas.This I-Corps project is based on the development of a novel sensor data modeling methodology. Sensor-based data generation is about to reach 73+ trillion gigabytes by 2025. Contemporary data collection and analysis pipelines are inefficient and uneconomical for this scale of data. This technology investigates undersampling for several orders of magnitude savings in data collection, transmission, storage, cloud computing, and analytics. Some application areas include the Industrial Internet of Things (IIoT), environmental and ocean monitoring, and autonomous drones and vehicles. The novel methodology in the post-Nyquist era information extraction pipeline centered around undersampling, low-dimensional latent representations, and machine learning, with several orders of savings in the data and engineering complexity of IIoT systems. The insight on undersampling-based learning of low-dimensional latent representations and using them to solve different downstream tasks is the intellectual contribution of this project.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.

该I-Corps项目的更广泛的影响/商业潜力是改善在日常生活中无处不在的传感器的数据，包括个人设备，智能家居和基础设施，工业机器和工厂，自动驾驶机器以及自然环境。基于传感器的“大数据”为各种系统提供了实时可见性。但是，这些数据的量，速度和多样性将很快淹没，完善和分析所有数据的能力。该项目旨在开发建模方法，以改善涉及嵌入式系统（例如物联网（IoT），太空探索和生物技术等嵌入式系统）中收集，处理和分析数据的方式。在海洋和生物圈中对工业机器和分布感应的远程监测是某些应用领域。这个I-Corps项目基于一种新型传感器数据建模方法的开发。到2025年，基于传感器的数据生成将达到73万亿千兆字节。当代数据收集和分析管道对于此数据规模而言效率低下且不可感知。该技术研究了在数据收集，传输，存储，云计算和分析中节省几个数量级的几个数量级的不足采样。一些应用领域包括工业互联网（IIOT），环境和海洋监测以及自动驾驶无人机和车辆。尼奎斯特后时代的信息提取管道中的新方法围绕不足采样，低维度表示和机器学习，并在IIOT系统的数据和工程复杂性中节省了几个顺序。对基于低维的潜在表示的学习的见解，并使用它们来解决不同的下游任务是该项目的智力贡献。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响来审查标准，通过评估来诚实地对支持。