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 These new code classes have superseded theperformance of random code ensembles used in network informationtheory since its inception.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.

“该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。“以充分实现基于无处不在的传感器节点的下一代通信网络基础设施的潜力为愿景，本研究引入了新的体系结构和策略，用于分布式网络信息处理，其直接利用：信息处理的客观性能指标的结构，传感节点收集的信息中的底层统计依赖关系的结构，网络的拓扑结构，以及双向交互信息交换的能力。本研究主张网络信息处理的两重范式转变：1）从传统的数据传输目标向函数计算目标的转变; 2）从单向信息流模型向交互信息流模型的转变。这项研究支持未来的科学家和工程师的教育，将研究进展与计算机发展相结合，并通过鼓励妇女和代表性不足的群体的参与来支持多样性。这项研究开发了两种全新的交互式信息处理代码集。第一种是基于抽象代数和随机图论的技术，以捕获在目的地计算的函数的结构。第二种是基于通信复杂性和多终端信息理论的技术来捕捉网络中信息流的交互结构。这些新的代码类已经取代了网络信息理论自成立以来使用的随机代码集成的性能。这项研究开发了新的分析框架和工具，以揭示传感器网络中交互式信息处理的基本性能限制。 这项研究通过提供在四个基本领域，即信息和编码理论、抽象代数、随机图论和通信复杂性理论之间建立桥梁的组件，促进了研究领域的交叉授粉。