CIF: Small: Distributed Function Computation and Multiterminal Data Compression

CIF：小型：分布式函数计算和多端数据压缩

• 批准号: 1117546
• 负责人: Prakash Narayan
• 金额: $ 41.62万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117546&HistoricalAwards=false
• 关键词: CIF; Small; Distributed; Function; Computation

This research addresses the theory and design of algorithms for an efficient local computation by multiple network terminals of shared functions of all their observed correlated data. Efficient communication among the terminals facilitates efficient computation. Applications include: computing the average, variance, maximum, minimum and parity of observed data in a colocated network of wireless sensors that make correlated measurements. This objective is connected closely to the design of algorithms for the efficient compression of data for storage and transmission purposes, as well as of algorithms for assuring data security. A main goal of the project is to characterize explicitly these connections, thereby leading to the development of new and efficient algorithms for data compression, function computation and network security.The technical approach involves a formulation of the underlying problems and their analysis, using an information theoretic framework. This will enable the development of a principle of "entropy decomposition of total shared randomness" in a network model to address difficult problems in multiuser information theory of which rate-efficient function computation is a leading example. In particular, an application of source coding algorithms in distributed function computation will be studied. Specific groups of open problems chosen for investigation address a general class of multiterminal models for function computation and data compression. This choice is motivated by, and is of compelling interest to, the theory and engineering practice of network function computation and source coding, as well as network security.

这项研究解决了多个网络终端对所有观察到的相关数据的共享功能进行有效本地计算的算法的理论和设计。终端之间的高效通信有利于高效计算。应用包括：计算进行相关测量的并置无线传感器网络中观察到的数据的平均值、方差、最大值、最小值和奇偶性。这一目标与用于存储和传输目的的有效数据压缩算法以及确保数据安全的算法的设计密切相关。该项目的主要目标是明确地描述这些联系，从而开发出用于数据压缩、函数计算和网络安全的新的高效算法。该技术方法涉及使用信息来制定根本问题及其分析。理论框架。这将使网络模型中“总共享随机性的熵分解”原理的发展成为可能，以解决多用户信息论中的难题，其中速率高效函数计算是一个主要例子。特别是，将研究源编码算法在分布式函数计算中的应用。选择用于研究的特定开放问题组解决了用于函数计算和数据压缩的通用多端模型。这一选择是由网络函数计算和源编码以及网络安全的理论和工程实践所激发的，并且引起了人们的极大兴趣。