CAREER: A Systems Approach to Networked Decision Making in Uncertain Environments

职业：不确定环境中网络决策的系统方法

• 批准号: 0449194
• 负责人: Venkatesh Saligrama
• 金额: --
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0449194&HistoricalAwards=false
• 关键词: CAREER; Systems; Approach; Networked; Decision

CAREER: A Systems Approach to Networked Decision Making inUncertain EnvironmentsThis proposal concerns the development of tools for a fundamental system-level framework fornetworked decision making in uncertain environments. While significant effort over the last decadein sensor development, physical layer transmission and networking has laid the initial groundworkfor practical deployment, the full potential for networked sensing systems can only be realizedthrough a fundamental understanding of decision-making in networked and uncertain environments.In keeping with this view, the proposed research will represent communications and networkingaspects as mathematical constraints and develop methods for a distributed group of decision agentsto reliably detect, localize, and track relevant dynamic and uncertain events under these constraints.Intellectual Merit: We organize our proposed research into four thrusts based on two funda-mentally different types of problems that arise in many (environmental) applications: (a) whereeach networked sensor/decision agent observes part of a global phenomena; (b)a local phenomenaobservable only by a small number of sensors. While the former case requires sensor collabora-tion across space, the latter requires rapidly searching for sensors with desired information in adecentralized manner. The four major thrusts are: Consensus Based Approach for Networked Decision Making: This thrust is concerned withdistributed inference when observations of global phenomena are involved. The approachamounts to local information refinement followed by local message passing. This is bothefficient and practically appealing. The solution techniques rely on properties of dynamicalsystems whose dynamics are characterized by network connectivity. Adaptive Decentralized Sampling: Here we address the localized information case. We pro-pose a feedback perspective that is inspired by recent developments in testing large numberof hypothesis in the statistics literature. Estimation under communication constraints: Here motivated by applications we developmixed stochastic/deterministic methods for communicating non-ergodic, non-random param-eters observed in stochastic noise. Dynamical Scenarios: In this thrust control theoretic methods for networked decision makingin highly dynamic scenarios is developed.Broad Impact: The proposed research has the potential to broadly impact existing knowledge inthe fields of networking, information theory, estimation, control, and signal processing as well asthe broader engineering community. In addition to disseminating our results through publicationsin scholarly journals, conference presentations and on-line we envision the following outcomes:Societal: Successful completion of the program will shed light on the way sensor networks areconfigured and operated for reliable, seamless and prolonged operation. The program is thereforeexpected to have societal impacts through the diverse applications such as emergency relief services,security/surveillance and other applications that closely relate to public welfare. Education: Weplan on introducing a first-year graduate level course that will seek an integrated viewpoint ofcontrol, networks and information.1

职业：不确定环境下网络化决策的系统方法这一建议涉及到不确定环境下网络化决策的基本系统级框架工具的开发。虽然过去十年在传感器开发、物理层传输和网络方面的重大努力为实际部署奠定了初步基础，但网络传感系统的全部潜力只能通过对网络和不确定环境中决策的基本理解来实现。所提出的研究将把通信和网络方面表示为数学约束，并为分布式决策代理组开发可靠地检测，定位，知识产权的优点：我们组织我们提出的研究到四个推力的基础上，在许多（环境）应用中出现的两个基本-mentally不同类型的问题：（a）其中每个网络传感器/决策代理观察到的全球现象的一部分;（B）一个局部现象只观察到少数传感器。前者要求传感器跨空间协作，后者要求以非集中方式快速搜索具有所需信息的传感器。这四个主要方面是：基于共识的网络决策方法：当涉及到对全局现象的观察时，这一推力与分布式推理有关。该方法相当于本地信息细化，然后是本地消息传递。这既有效率，又实际上很吸引人。求解技术依赖于动态系统的性质，其动态特性是由网络连通性表征的。自适应分散采样：这里我们解决本地化信息的情况。我们提出了一个反馈的角度来看，在测试大量的假设在统计文献中的最新发展的启发。通信约束下的估计：在这里，出于应用的动机，我们开发了混合随机/确定性方法，用于在随机噪声中观察到的非遍历、非随机参数的通信。动态场景：在这个推力控制理论的方法，网络决策在高度动态的scenaries.Broad影响：拟议的研究有可能广泛影响现有的知识在网络，信息理论，估计，控制和信号处理以及更广泛的工程界。除了通过学术期刊，会议报告和在线出版物传播我们的结果，我们设想以下成果：社会：该计划的成功完成将阐明传感器网络的配置和操作方式，以实现可靠，无缝和长期的操作。因此，该计划预计将通过各种应用产生社会影响，如紧急救援服务，安全/监视和其他与公共福利密切相关的应用。教育：我们计划引入一年级的研究生课程，以寻求控制，网络和信息的综合观点。