Sensors and Sensor Networks: A Control and Optimization Science Base for Sensor Networks in Adverse and Stochastic Environments

传感器和传感器网络：不利和随机环境中传感器网络的控制和优化科学基础

• 批准号: 0330171
• 负责人: Christos Cassandras
• 金额: $ 248.75万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0330171&HistoricalAwards=false
• 关键词: Sensors; Sensor; Networks; Control; Optimization

The goal of this project is to develop the foundations of a control and optimization science base for sensor networks viewed as complex systems operating in an uncertain and potentially adverse environment. The approach taken is a combination of addressing fundamental research issues while maintaining a focus on a specific target application domain, a manufacturing enterprise, in order to generate concrete, implementable results. The proposed research plan adopts three complementary, interconnected views of a sensor network: as a collection of elements that need to communicate among themselves, as a dynamic system whose control depends on a thorough understanding of element interactions, and as a multi-time scale distributed system that requires a layer of coordination. The analytical and computational challenges involved in this plan will be faced through novel modeling, estimation, and optimization approaches. An important component of the project is the development of an experimental sensor network testbed based on several low-power digital signal processors and microcontrollers. A mobile robot squadron will provide multi-sensor platforms networked with a group of laptop computers serving as the processing nodes of the resulting system. Research results will include new sensors for manufacturing systems, new energy saving protocols for wireless sensor networks, and new modeling and control approaches for complex engineering systems, all validated on this testbed.The project will advance the state-of-the-art in all application domains that can benefit from sensor networks, primarily focusing on manufacturing where advances will result in increased energy efficiency, accelerated productivity growth, improved product quality, and enhanced workplace safety and security. Other societal applications include transportation safety and homeland security. On the educational front, plans include multidisciplinary training of graduate and undergraduate students, creating software modules for educational case studies, and working to reach out to high school students.

该项目的目标是为被视为在不确定和潜在不利环境中运行的复杂系统的传感器网络开发控制和优化科学基础的基础。所采取的方法是解决基础研究问题的组合，同时保持对特定目标应用领域，制造企业的重点，以产生具体的，可实施的结果。拟议的研究计划采用了三个互补的，相互关联的传感器网络的观点：作为一个集合的元素，需要相互之间进行通信，作为一个动态系统，其控制取决于对元素交互的透彻理解，并作为一个多时间尺度的分布式系统，需要一层的协调。该计划中涉及的分析和计算挑战将通过新的建模，估计和优化方法来面对。该项目的一个重要组成部分是开发一个基于几个低功耗数字信号处理器和微控制器的实验传感器网络测试平台。一个移动的机器人中队将提供多传感器平台，该平台与一组膝上型计算机联网，作为最终系统的处理节点。研究成果将包括用于制造系统的新传感器、用于无线传感器网络的新节能协议以及用于复杂工程系统的新建模和控制方法，所有这些都将在该试验台上得到验证。该项目将推动所有可受益于传感器网络的应用领域的最新技术，主要集中在制造业，这些领域的进步将导致能源效率的提高，生产率的加速增长，提高产品质量，加强工作场所的安全和安保。其他社会应用包括运输安全和国土安全。在教育方面，计划包括对研究生和本科生进行多学科培训，为教育案例研究创建软件模块，并努力接触高中生。