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

摘要（限250字）：该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助。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，包括那些直接与物理世界交互的数据和系统。连接性的增强导致更容易受到攻击、故障和故障的影响，这些攻击、故障和故障可以沿着网络链路沿着更快地级联。该项目开发技术，以提高新兴的网络基础设施的可靠性，其中计算，通信和传感密切交织在一起。使用数据挖掘技术进行调查，以确定和消除涉及级联故障和传播的性能问题的情况。新兴的网络化和普适计算系统的复杂性增加了维护成本，挑战了经典的设计方法，并使传统的诊断和调试工具在捕捉问题时变得不那么有效。为了扭转这些趋势，该项目开发了专门适合于解决复杂分布式系统的三个基本挑战的工具，即不可再现的随机行为，高交互复杂性和物理资源约束。除了提高可靠性外，这项研究还与伊利诺伊大学的教育课程相结合，为研究生和本科生提供了现实世界的挑战，同时寻求鼓励文化多样性和促进妇女和少数民族参与工程的途径。实验室模块允许学生实验和诊断现实世界的设计问题和级联交互异常在一个动手的方式。该项目将导致改进的版本的数据挖掘教科书的共同PI，这是目前被认为是在该领域的标准参考。

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.