CIF: Collaborative Research:Small: Distributed Detection Algorithms and Stochastic Modeling for Large Monitoring Sensor Networks

CIF：协作研究：小型：大型监控传感器网络的分布式检测算法和随机建模

• 批准号: 1115769
• 负责人: Xuanlong Nguyen
• 金额: $ 26.35万
• 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: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1115769&HistoricalAwards=false
• 关键词: CIF; Collaborative; Research; Small; Distributed

Operational and safety goals for the built environment demand robust, scalable and reliable largescale monitoring for infrastructure systems. High performance real-time event detection and decision makingrequires models and algorithms to process large amounts of data from dense sensor networks deployedin these systems. Despite advances in the development of detection algorithms for such networks, there aretwo widely recognized and conflicting obstacles: detection rules need to be sufficiently complex to adapt tothe spatiotemporal changes in the environment, requiring the sharing of data; but rules are constrained bystatistical performance guarantees and computation and communications budgets imposed by the network.This project addresses these challenges by developing a fundamentally new approach that jointly accountsfor statistical detection, communication constraints and distributed computation.This research develops a framework that integrates the distributed computation and communication constraintsof the underlying network infrastructure with flexible stochastic modeling and learning algorithmswith spatiotemporal data. The modeling and algorithms enable simultaneous and sequential decision makingat many local sites, by borrowing information across the network in a statistically coherent and computationallyefficient manner. Combining the formalism of sequential change point detection, nonparametric andprobabilistic graphical models and spatiotemporal statistics, the project develops distributed and sequentialmessage-passing algorithms for detecting changes in the underlying distributions generating network data.The models developed also offer new theoretical understanding of the trade-offs between statistical modelcomplexity, distributed computation efficiency, and structure of communication constraints within the network.This interdisciplinary research brings together students and researchers from different areas, utilizingand developing knowledge and cross-disciplinary skills in the fields of computer science, statistics, signalprocessing and civil engineering.

建筑环境的运营和安全目标需要对基础设施系统进行强大、可扩展和可靠的大规模监控。高性能实时事件检测和决策需要模型和算法来处理来自这些系统中部署的密集传感器网络的大量数据。尽管此类网络的检测算法的开发取得了进展，但仍然存在两个被广泛认可且相互冲突的障碍：检测规则需要足够复杂以适应环境的时空变化，需要共享数据；但规则受到统计性能保证以及网络施加的计算和通信预算的约束。该项目通过开发一种全新的方法来解决这些挑战，该方法共同考虑统计检测、通信约束和分布式计算。本研究开发了一个框架，将底层网络基础设施的分布式计算和通信约束与灵活的随机建模和时空学习算法集成在一起 数据。建模和算法通过以统计上一致且计算高效的方式借用网络信息，在许多本地站点实现同步和顺序决策。该项目结合了顺序变化点检测、非参数和概率图形模型以及时空统计的形式，开发了分布式和顺序消息传递算法，用于检测生成网络数据的底层分布的变化。所开发的模型还为统计模型复杂性、分布式计算效率和网络内通信约束结构之间的权衡提供了新的理论理解。 跨学科研究汇集了来自不同领域的学生和研究人员，利用和发展计算机科学、统计学、信号处理和土木工程领域的知识和跨学科技能。