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

CIF：小型：协作研究：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: 1116377
• 负责人: Ram Rajagopal
• 金额: $ 23.5万
• 依托单位: Stanford University
• 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 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116377&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; 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 communicational 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.

建筑环境的操作和安全目标要求对基础设施系统的强大，可扩展和可靠的大规模监控。高性能实时事件检测和决策制定模型和算法，以处理这些系统的密集传感器网络中的大量数据。尽管开发了此类网络的检测算法的发展，但仍存在广泛认可和相互矛盾的障碍：检测规则需要足够复杂，以适应环境中的时空变化，需要数据共享；但是，规则受到网络施加的限制性绩效保证和计算和沟通预算。该项目通过开发一种从根本上进行统计检测，沟通约束和分布式计算的基本新方法来解决这些挑战。这项研究开发了一项框架，该框架集成了分布式计算的框架以及具有灵活的随机建模和学习算法时空数据的基础网络基础架构的通信约束。该建模和算法通过以统计相干和计算效率的方式借入网络上的信息来实现许多本地站点的同时决策。结合了顺序变化点检测的形式主义，非参数和稳态图形模型以及时空统计数据，该项目开发了分布式和顺序逐渐通过的算法，用于检测基本分布的变化，该模型还提供了对权衡的新理论理解在网络内的统计模型复杂性，分布式计算效率和沟通约束的结构之间。这项跨学科研究将来自不同领域的学生和研究人员汇集在一起​​，利用并在计算机科学，统计，信号处理和土木工程领域发展知识和跨学科技能。