NetSE: Medium: A Data Mining Approach to Diagnostic Debugging in Sensor Networks

NetSE：Medium：传感器网络中诊断调试的数据挖掘方法

• 批准号: 0905014
• 负责人: Tarek Abdelzaher
• 金额: $ 100.16万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905014&HistoricalAwards=false
• 关键词: NetSE; Medium; Data; Mining; Approach

Abstract (limited to 250 words): This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Our nation's infrastructure relies increasingly on networks that connect growing amounts of data and systems, including those that interact directly with the physical world. Increased connectivity leads to a higher degree of vulnerability to attacks, malfunctions, and failures that can cascade more rapidly along network links. The project develops techniques to improve the reliability of emerging networked infrastructure, where computation, communication, and sensing are intimately intertwined. Use of data mining techniques is investigated to determine and eliminate scenarios involving cascaded failures and propagation of performance problems. The complexity of emerging networked and pervasive computing systems increases maintenance cost, challenges classical design approaches, and makes traditional diagnostics and debugging tools less effective at catching problems. To reverse these trends, this project develops tools that are specifically suited to address three fundamental challenges of complex distributed systems; namely, non-reproducible stochastic behavior, high interactive complexity, and physical resource constraints. Other than improving reliability, this research is integrated with education curricula at the University of Illinois, offering real-world challenges to intellectually stimulate both graduate and undergraduate students, while seeking avenues to encourage cultural diversity and promote women and minority involvement in engineering. Laboratory modules allow students to experiment with and diagnose real-world design problems and cascading interaction anomalies in a hands-on fashion. The project will result in improved versions of a data mining textbook by the Co-PI, which is currently considered the standard reference in the field.

摘要（限制为250个单词）：该奖项是根据2009年《美国复苏与再投资法》（公法111-5）资助的。我们国家的基础设施越来越依赖于连接越来越多的数据和系统的网络，包括直接与物理世界互动的网络。连通性的提高导致更高的攻击，故障和失败的脆弱性，这些攻击性可以更快地沿网络链接级联。该项目开发了提高新兴网络基础架构的可靠性的技术，其中计算，通信和传感密切相互交织。研究了数据挖掘技术的使用，以确定和消除涉及级联故障和绩效问题传播的场景。新兴网络和普遍计算系统的复杂性增加了维护成本，挑战经典的设计方法，并使传统的诊断和调试工具在捕获问题方面的有效性降低。为了扭转这些趋势，该项目开发了专门针对复杂分布式系统的三个基本挑战的工具；也就是说，不可复制的随机行为，高度互动复杂性和物理资源约束。除了提高可靠性之外，这项研究还与伊利诺伊大学的教育课程融合在一起，在智力上提出了现实世界中的挑战，以刺激研究生和本科生，同时寻求途径来鼓励文化多样性，并促进妇女和少数派参与工程。实验室模块允许学生尝试和诊断现实世界的设计问题，并以动手方式进行互动异常。该项目将导致Co-Pi的数据挖掘教科书的改进版本，目前被视为现场中的标准参考。