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

职业生涯：不确定环境中网络决策的系统方法本提案涉及为不确定环境中的网络决策开发基本系统级框架的工具。在过去的十年中，传感器开发、物理层传输和联网已经为实际部署奠定了最初的基础，但网络化传感系统的全部潜力只有通过对联网和不确定环境中的决策的基本理解才能实现。根据这一观点，拟议的研究将把通信和联网方面表示为数学约束，并为分布式决策组开发方法，以可靠地检测、定位、并在这些约束下跟踪相关的动态和不确定事件。智力上的优点：我们基于在许多(环境)应用中出现的两种基本的不同类型的问题，将我们提出的研究组织成四个推进：(A)每个联网的传感器/决策代理观察全球现象的一部分；(B)只有少数传感器才能观察到的局部现象。前一种情况需要传感器跨空间协作，而后一种情况则需要以集中式方式快速搜索具有所需信息的传感器。四个主要推动力是：基于共识的网络决策方法：当涉及到对全球现象的观察时，这一推力涉及分布式推理。这种方法先进行局部信息精化，然后再进行局部消息传递。这既是有效的，实际上也是有吸引力的。求解技术依赖于动态系统的特性，动态系统的动态特性以网络连通性为特征。自适应分散抽样：这里我们讨论局部化信息情况。我们提出了一种反馈观点，该观点受到统计文献中大量假设检验的最近发展的启发。通信约束下的估计：在这里，受应用的启发，我们发展了混合的随机/确定性方法来传递在随机噪声中观察到的非遍历的、非随机的参数。动态场景：在这个推力控制理论中，发展了高度动态场景中的网络决策方法。广泛的影响：拟议的研究具有广泛影响网络、信息论、估计、控制和信号处理领域的现有知识以及更广泛的工程社区的潜力。除了通过在学术期刊、会议演讲和在线上发表文章来传播我们的成果外，我们还展望了以下成果：社会：该项目的成功完成将揭示传感器网络的配置和运行方式，以实现可靠、无缝和长时间的运行。因此，预计该计划将通过各种应用产生社会影响，如紧急救济服务、安全/监视和其他与公共福利密切相关的应用。教育：我们计划推出一年级研究生水平课程，寻求控制、网络和信息的综合观点1

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