CIF: Small: Data Reduction for Networked Inference

CIF：小：网络推理的数据缩减

• 批准号: 1218289
• 负责人: Biao Chen
• 金额: $ 41.68万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218289&HistoricalAwards=false
• 关键词: CIF; Small; Data; Reduction; Networked

Technological advances in devices, computing, communication, and networking have allowed the development and deployment of networked sensing systems of varying scales with diverse applications. The voluminous data collected in these networked systems, however, present some unique challenges. Ensuring that useful data be available at the right location in a timely fashion is difficult in a large scale network because of various limitations inherent in such systems. In addition, the collected data in its raw form often lacks clarity for making an informed decision. The primary goal of the project is to develop theory and methodology that allow the raw data to be reduced to the extent such that 1) their transmission to the desired destination is compliant to the system limitation, and 2) there is no loss of information with respect to the mission of the networked system. The project pursues a comprehensive treatment of data reduction in such inference networks and aims to develop the sufficiency principle and related theory and methods to overcome these challenges. Intimate connection between data reduction for inference and data compression for transmission is exploited and expanded in the network setting. New interpretations using the sufficiency principle for some classical multi-terminal source coding problems will be developed; they provide a new venue for the exploration of the connection between statistics and information theory. The development of theory and methods that guide data reduction in a networked inference system also has the potential to unify existing works that are tailored toward specific inference problems.

设备、计算、通信和网络方面的技术进步使得不同规模和不同应用的网络传感系统的开发和部署成为可能。然而，这些网络系统中收集的大量数据提出了一些独特的挑战。在大规模网络中，由于此类系统固有的各种限制，确保在正确的位置及时提供有用的数据是很困难的。此外，收集到的原始数据通常缺乏清晰度，无法做出明智的决策。该项目的主要目标是开发理论和方法，使原始数据减少到以下程度：1）它们到所需目的地的传输符合系统限制，2）不会丢失与网络系统的任务相关的信息。该项目追求对此类推理网络中的数据缩减进行综合处理，旨在发展充分性原则和相关理论和方法来克服这些挑战。在网络设置中利用和扩展了用于推理的数据缩减和用于传输的数据压缩之间的密切联系。将使用充分性原理对一些经典的多终端源编码问题进行新的解释；它们为探索统计与信息论之间的联系提供了新的场所。指导网络推理系统中的数据缩减的理论和方法的发展也有可能统一针对特定推理问题定制的现有工作。