CIF: Small: Collaborative Research: Towards a Paradigm-shift in Distributed Information Processing: Harnessing Group-structure and Interaction

CIF：小型：协作研究：迈向分布式信息处理的范式转变：利用群体结构和交互

• 批准号: 0915619
• 负责人: Sandeep Sadanandarao
• 金额: $ 25万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915619&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Towards

"This award is funded under the American Recovery and Reinvestment Act of 2009(Public Law 111-5)."With a vision to fully realize the potential of next generationcommunication network infrastructure based on ubiquitous sensor nodes,this research introduces new architectures and strategies, fordistributed in-network information processing, which directly harness:the structure of the objective performance metrics for informationprocessing, the structure of the underlying statistical dependenciesin the information gathered by sensing nodes, the topology of thenetwork, and the capability for bidirectional interactive informationexchange. This research advocates a two-fold paradigm shift in networkinformation processing: 1) a shift from the traditional goal of datatransport to the goal of function computation and 2) a shift fromunidirectional models of information flow to interactive informationflow models. This research supports the education of future scientistsand engineers by integrating research advances with curriculumdevelopment and supports diversity by encouraging the participation ofwomen and under-represented groups.This research develops two fundamentally new classes of code ensemblesfor interactive information processing. The first is based ontechniques from abstract algebra and random graph theory to capturethe structure of functions being computed at destinations. The secondis based on techniques from communication complexity and multiterminalinformation theory to capture interactive structures of informationflow in the network. These new code classes have superseded theperformance of random code ensembles used in network informationtheory since its inception. This research develops new analyticalframeworks and tools to uncover the fundamental performance limits ofinteractive information processing in sensor networks. This researchfacilitates the cross-pollination of research fields by providingcomponents which build bridges between four fundamental areas, namely,information and coding theory, abstract algebra, random graph theory,and communication complexity theory.

"This award is funded under the American Recovery and Reinvestment Act of 2009(Public Law 111-5)."With a vision to fully realize the potential of next generationcommunication network infrastructure based on ubiquitous sensor nodes,this research introduces new architectures and strategies, fordistributed in-network information processing, which directly harness:the structure of the objective performance metrics for informationprocessing, the structure of the underlying统计依赖项在通过传感节点，thenetwork的拓扑以及双向交互式信息交换的能力收集的信息中。这项研究主张网络信息处理的两倍范式转移：1）从DataTransport的传统目标转变为功能计算目标的转变，以及2）从信息流的不转移模型转移到交互式信息流模型。这项研究通过将研究进步与课程开发整合在一起，支持未来科学家和工程师的教育，并通过鼓励妇女的参与和代表性不足的群体来支持多样性。这项研究开发了两种基本的新型Code Ensembles，用于交互式信息处理。首先是基于从抽象代数和随机图理论到在目的地计算的函数结构的本地技术。基于通信复杂性和多性信息理论的技术，以捕获网络中信息流的交互式结构。这些新的代码类已取代自成立以来网络信息理论中使用的随机代码合奏的性能。这项研究开发了新的分析框架和工具，以发现传感器网络中Intertractive信息处理的基本性能限制。 这项研究通过提供了四个基本领域之间的桥梁，即信息和编码理论，摘要代数，随机图理论和通信复杂性理论，从而促进了研究领域的交叉授粉。