Distributed Methods for Statistical Decision Making in Networked Environments

网络环境中统计决策的分布式方法

• 批准号: 0430983
• 负责人: Murat Alanyali
• 金额: --
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0430983&HistoricalAwards=false
• 关键词: Distributed; Methods; Statistical; Decision; Making

Abstract: Recent advances in sensor and computing technologies enable massively distributed networks of sensors as candidate technologies to provide real-time information in diverse applications such as building safety, environmental sensing, habitat monitoring, power systems and manufacturing. The potential of networked sensing by such devices is enormous, yet there are significant system-level challenges in realizing this potential by designs that are reliable, efficient and scalable.This research involves the development of fundamental systems-level framework for uncertain decision making in ad-hoc networked and power limited environments. Although some of these issues have beenpreviously tackled in isolation, we will develop a unified approach, addressing multiple interacting dependencies to obtain systems level tools for rational design and implementation. Accordingly this research has three principle thrusts: 1) Adaptive Querying Strategies (AQS) -- a top-down viewpoint based on hierarchical architectures that involve designated decision agents(s). In this thrust we propose novel feedback strategies for sensor querying at low data rates (low granularity). 2) Distributed Detection -- a bottom-up approach which amounts to consensus building via local message passing on sub-networks. 3) Distributed Estimation -- Strategies for scaling information granularity from a set of known sensors to known destinations under operational constraints is proposed. The novelty of this approach is to develop tools for sensor fusion for transmitting information about non-ergodic, non-random parameters observed in stochastic noise.

摘要：传感器和计算技术的最新进展使大规模分布式传感器网络成为候选技术，可以在建筑安全、环境传感、生境监测、电力系统和制造业等不同应用中提供实时信息。通过这类设备实现网络感知的潜力是巨大的，但通过可靠、高效和可扩展的设计来实现这种潜力存在着巨大的系统级挑战。本研究涉及到在自组织网络和功率有限的环境中开发基本的系统级不确定决策框架。尽管其中一些问题以前是单独解决的，但我们将开发一种统一的方法，解决多个相互作用的依赖关系，以获得用于Rational设计和实现的系统级工具。相应地，本研究有三个主要推动力：1)自适应查询策略(AQS)--一种基于指定决策主体的层次化结构的自上而下的观点(S)。在这个推力中，我们提出了新的反馈策略，用于低数据率(低粒度)的传感器查询。2)分布式检测--一种自下而上的方法，通过在子网络上传递本地消息来建立共识。3)分布式估计--提出了在操作约束下将信息粒度从一组已知传感器扩展到已知目的地的策略。这种方法的创新之处在于开发了传感器融合工具，用于传输关于随机噪声中观察到的非遍历、非随机参数的信息。