EAGER: Heterogeneous Sensor Network Design and Information Integration

EAGER：异构传感器网络设计和信息集成

• 批准号: 0956438
• 负责人: Qilian Liang
• 金额: $ 10万
• 依托单位: University of Texas at Arlington
• 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: 2009-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0956438&HistoricalAwards=false
• 关键词: EAGER; Heterogeneous; Sensor; Network; Design

Intellectual Merits: The objective of this project is to develop information theory and signal processing-based algorithms for Heterogeneous Sensor Networks (HSN) that jointly addresses sensor adaptation and data fusion in the presence of a combination of stationary or non-stationary heterogeneous sensors in constrained environments. Two research thrusts will be investigated: 1) information theory for HSN design; 2) signal processing for HSN information integration. Current research on the theory and algorithms of HSN is very limited. There exist a number of fundamental problems that have never been addressed, yet they are critical to the HSN. To tackle these problems, novel approaches and methodologies that will incorporate techniques such as information theory, network science, collaborative signal processing, estimation theory, statistical approach, and optimization will be studied and therefore has the potential to inspire transformative methodologies that could be applied to many domains. The proposed theories will lead to practical methodologies, algorithms and design tools with performance robust to uncertainty and adaptive to variations in dynamic operating conditions.Broader Impacts: This project will make a significant contribution to the applications of HSN and will have a broad and deep social impact. Because of the vast amounts of real-time information coming from many widely distributed sources in homeland security and defense, HSN is needed to collect and process threat-related information to insure that it is not overlooked. A carefully developed, research-centric education plan is also proposed. Under-represented and female students will be recruited from different societies for this project.

智能优点：该项目的目标是为异类传感器网络(HSN)开发基于信息理论和信号处理的算法，在约束环境中存在静态或非静态的异质传感器组合的情况下，共同解决传感器自适应和数据融合问题。将研究两个方面：1)用于HSN设计的信息论；2)用于HSN信息集成的信号处理。目前对HSN的理论和算法的研究非常有限。有许多根本问题从未得到解决，但它们对HSN至关重要。为了解决这些问题，将研究新的途径和方法，这些途径和方法将结合信息理论、网络科学、协同信号处理、估计理论、统计方法和优化等技术，因此有可能启发可应用于许多领域的变革方法。所提出的理论将产生实用的方法、算法和设计工具，其性能对不确定性和动态运行条件的变化具有鲁棒性。广泛的影响：该项目将为HSN的应用做出重大贡献，并将产生广泛而深刻的社会影响。由于在国土安全和国防中，海量的实时信息来自许多广泛分布的来源，因此需要HSN来收集和处理与威胁相关的信息，以确保其不被忽视。还提出了一个精心制定的、以研究为中心的教育计划。这一项目将从不同的社团招募代表不足的女学生。