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

爱荷华大学和德克萨斯a&m大学的合作研究项目的目标是提供解决方案，使集群传感器网络的有效容错分析成为可能，并进一步建立一种设计方法，以实现强大可靠的系统。该方法是通过考虑嵌入在大多数真实传感器网络中的簇结构来探索有效的解决方案。为了大大降低容错分析的计算复杂度，将为聚类传感器网络设计许多基于分解的算法。此外，有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。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健壮和可靠运行的理解。这项基础研究将广泛地促进射频识别网络在制造企业中的应用，以及用于物流和运输控制的其他类型的无线传感器网络。容错分析和设计方法的应用将使分布式传感器网络可靠运行，减少误报的发生。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从事职业，并聘请少数民族高中的教师，为研究和教育的基础设施做出贡献。本项目产生的方法将通过出版物、研讨会和讲习班广泛传播。

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.