The research targets fundamental work in sensor networks. Specifically the work addresses the central problem that distributed inference systems need to be robust to a wide variety of sensing, and channel impairments. The effort seeks to design a suite of robust, distributed detection and estimation algorithms for sensor networks and Net-Centric applications. A comprehensive approach to robust distributed inference for a wide range of communication channels and topologies, with minimal assumptions about the sensing noise, is proposed.

The outcomes of the proposed work have the potential to greatly impact many Net-Centric applications including environmental, military and health care that require aggregation of data from sensors which are linked only through wireless means. The work is supported by the Industry Advisory Board as well as individual industry members of the center and has the potential to extend the center?s portfolio while potentially attracting new members. The PI has collaborative TUES Phase 3 software project with HBCU colleges that will be tapped on for recruitment of minority students for training in the Ph.D. program associated with this I/UCRC FRP.