CAREER: Streaming Data Analysis in Sensor Networks

职业：传感器网络中的流数据分析

• 批准号: 0954704
• 负责人: Yajun Mei
• 金额: $ 40万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0954704&HistoricalAwards=false
• 关键词: CAREER; Streaming; Data; Analysis; Sensor

This research aims to offer statistical foundation and a host of efficient scalable methodologies for streaming data analysis in sensor networks. In many applications, sensor networks are deployed to online monitoring of changing environments over time and space, with a goal of early detection of some particular trigger events that can cause significant damage. However, the nature of streaming data from distributed, diverse sources and the constrained network resources (on communication, computing, costs, privacy of raw data, etc.) pose significant challenges, which require the development of new statistical tools, methods and theories. In this project, the investigator proposes a novel general framework for monitoring sensor networks in which a trigger event may affect different sensors or data streams differently. Some specific research topics include pure (consensus or parallel) detection and inference after detection, under different scenarios, depending on the models for sensor observations and the design requirements of sensor protocols. In addition, the research will integrate research and education by infusing the research findings into the curriculum, by organizing seminars and workshops, and by advising graduate and undergraduate students.Senor networks have broad real-world applications, including but not limited to health and environmental monitoring, biomedical signal processing, wireless communication, intrusion detection in computer networks, and biosurveillance. On the one hand, this research project will offer crucial statistical tools to effectively and efficiently monitor and analyze dynamic data streams in these sensor network applications. On the other hand, it also has a frustrating yet profound implication in these applications: Faced with the limitations implied by the (asymptotic) optimality theories of the proposed research, practitioners and researchers may need to constantly look for better data sources to achieve desired system performance in their specific applications rather than relying on an improved methodology for existing data sources.

本研究旨在为传感器网络中流数据分析提供统计基础和一系列高效可扩展的方法。在许多应用中，传感器网络被部署用于在线监测随时间和空间变化的环境，目的是及早发现一些可能造成重大破坏的特定触发事件。然而，来自分布式、不同来源的流数据的性质以及受限的网络资源(在通信、计算、成本、原始数据的隐私等方面)提出了重大挑战，这就需要开发新的统计工具、方法和理论。在这个项目中，研究人员提出了一个新颖的通用框架来监控传感器网络，其中触发事件可能会对不同的传感器或数据流产生不同的影响。一些具体的研究课题包括纯(一致或并行)检测和检测后推理，在不同的场景下，取决于传感器观测的模型和传感器协议的设计要求。此外，这项研究将把研究成果融入课程，通过组织研讨会和研讨会，并通过为研究生和本科生提供建议，将研究和教育结合在一起。传感器网络在现实世界中有广泛的应用，包括但不限于健康和环境监测、生物医学信号处理、无线通信、计算机网络中的入侵检测和生物监控。一方面，该研究项目将提供关键的统计工具，以有效和高效地监测和分析这些传感器网络应用中的动态数据流。另一方面，它在这些应用中也有令人沮丧但深刻的影响：面对拟议研究的(渐近)最优性理论所隐含的限制，从业者和研究人员可能需要不断寻找更好的数据源，以在其特定应用中实现所需的系统性能，而不是依赖于现有数据源的改进方法。