CAREER: Coordinated Data-Driven Modeling and Optimization for Wireless Sensor Networks

职业：无线传感器网络的协调数据驱动建模和优化

• 批准号: 0644289
• 负责人: Farinaz Koushanfar
• 金额: $ 40万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0644289&HistoricalAwards=false
• 关键词: CAREER; Coordinated; Data; Driven; Modeling

The need for comprehensive monitoring and analysis of complex environments has brought wireless sensor networks (WSNs) to the forefront of research. To be effective in practice, WSNs need to capture system state, monitor complex properties, and model physical phenomena accurately and robustly, without any biases. Currently, most efforts in this area employ parametric models and other a priori assumptions that postulate known phenomena and system properties. The characteristics of emerging complex applications, however, are often unknown and hard to predict. Thus, optimizations using models based on a priori assumptions often have limited effectiveness.This research plan develops novel data-driven modeling methods, scalable modular structures, and application optimization algorithms that comprehensively abstract, capture and operate on traces of deployed WSNs. While the research thrusts are intended to provide applicable models, development tools, and system software for WSNs, they simultaneously advance the theory and practice of statistical modeling and optimization algorithms. The models and algorithms are coordinated: the models are built so that they facilitate consequent optimizations, while the optimization algorithms are created in such a way that they take into account the structure and uncertainties of the models. The results are intrinsically interdisciplinary and have impact on a broad range of other fields. This project integrates research and educational activities by engaging students in the research and by incorporating the research into both undergraduate and graduate levels. The research results and educational material are promptly disseminated via academic publications, the Connexions platform, and other WWW sources.

对复杂环境进行全面监控和分析的需求使无线传感器网络（WSN）成为研究的最前沿。为了在实践中有效，WSN需要捕获系统状态，监测复杂的特性，并准确，稳健地模拟物理现象，而没有任何偏见。当前，该领域的大多数努力都采用参数模型和其他假定已知现象和系统属性的先验假设。但是，新兴复杂应用的特征通常是未知的，也很难预测。因此，使用基于先验假设的模型进行优化通常具有有限的有效性。本研究计划开发了新颖的数据驱动建模方法，可扩展的模块化结构以及应用优化算法，这些算法可以全面地抽象，捕获和操作已部署的WSN的痕迹。尽管研究推力旨在为WSN提供适用的模型，开发工具和系统软件，但它们同时推进了统计建模和优化算法的理论和实践。模型和算法是协调的：构建了模型，以便它们促进了随后的优化，而优化算法的创建方式是以此考虑模型的结构和不确定性的方式。结果在本质上是跨学科的，并对其他广泛领域产生了影响。该项目通过吸引学生参与研究并将研究纳入本科和研究生水平来整合研究和教育活动。研究结果和教育材料通过学术出版物，连接平台和其他WWW来源迅速传播。