Collaborative Research: Fault Tolerance Analysis and Design of Clustered Sensor Networks

协作研究：集群传感器网络容错分析与设计

• 批准号: 0727305
• 负责人: Yu Ding
• 金额: $ 16.13万
• 依托单位: Texas A&M Engineering Experiment Station
• 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: 2007-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0727305&HistoricalAwards=false
• 关键词: Collaborative; Research; Fault; Tolerance; Analysis

The objective of this collaborative research project between the University of Iowa and Texas A&M University is to provide solutions that enable efficient fault-tolerance analysis of clustered sensor networks and further establish a design method for achieving a robust and reliable system. The approach is to explore efficient solutions by considering the cluster structures embedded in most real-world sensor networks. A number of decomposition-based algorithms will be designed for clustered sensor networks to substantially reduce the computational complexity in fault tolerance analysis. In addition, efficient Monte Carlo sampling and heuristics methods will be applied for design evaluation and optimization of sensor networks. The models and algorithms will be validated and tested using both wireless sensor networks in laboratory environments and real-world distributed sensor systems in industry.If successful, the project will enhance the understanding of the robust and reliable operations of distributed sensor networks. This fundamental research will contribute broadly to the applications of radio-frequency identification networks used in manufacturing enterprises and other types of wireless sensor networks used for logistics and transportation control. The application of fault tolerance analysis and design methods will lead to reliable operations and reduce the occurrence of false positives in distributed sensor networks. Meanwhile, the collaborative nature of this research will provide students a multidisciplinary training and will bring industrial perspective to the universities. It will contribute to the infrastructure for research and education by upgrading the current research labs, motivating students to pursue careers in relevant areas, and engaging teachers from minority-serving high schools. The resulting methodologies from this project will be broadly disseminated through publications, seminars, and workshops.

爱荷华大学和德克萨斯农工大学之间的这个合作研究项目的目标是提供能够对集群传感器网络进行高效容错分析的解决方案，并进一步建立一种实现稳健可靠系统的设计方法。该方法是通过考虑大多数现实世界传感器网络中嵌入的集群结构来探索有效的解决方案。将为集群传感器网络设计许多基于分解的算法，以大幅降低容错分析中的计算复杂度。此外，高效的蒙特卡罗采样和启发式方法将应用于传感器网络的设计评估和优化。这些模型和算法将使用实验室环境中的无线传感器网络和工业中的真实分布式传感器系统进行验证和测试。如果成功，该项目将增强对分布式传感器网络稳健可靠运行的理解。这项基础研究将对制造企业中使用的射频识别网络以及用于物流和运输控制的其他类型的无线传感器网络的应用做出广泛贡献。容错分析和设计方法的应用将导致分布式传感器网络的可靠运行并减少误报的发生。同时，这项研究的协作性质将为学生提供多学科培训，并将为大学带来行业视角。它将通过升级现有的研究实验室、激励学生在相关领域从事职业以及聘请少数族裔高中的教师，为研究和教育基础设施做出贡献。该项目得出的方法将通过出版物、研讨会和讲习班广泛传播。