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

爱荷华州和得克萨斯州A M大学之间的合作研究项目的目的是提供解决方案，使有效的容错分析的集群传感器网络，并进一步建立一个设计方法，实现一个强大的和可靠的系统。该方法是通过考虑嵌入在大多数现实世界的传感器网络的集群结构，探索有效的解决方案。为了降低容错分析的计算复杂度，将设计一些基于分解的算法。此外，有效的蒙特卡罗抽样和并行计算方法将被应用于传感器网络的设计评估和优化。这些模型和算法将在实验室环境中的无线传感器网络和工业中的实际分布式传感器系统中进行验证和测试。如果成功，该项目将提高对 分布式传感器网络该基础研究将为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可靠的运行和减少误报的发生在分布式传感器网络。同时，这项研究的合作性质将为学生提供多学科的培训，并将为大学带来工业视角。它将通过升级现有的研究实验室，激励学生在相关领域追求职业生涯，并吸引少数民族高中的教师，为研究和教育的基础设施做出贡献。将通过出版物、研讨会和讲习班广泛传播这一项目产生的方法。

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.