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.
