Wireless sensor networks deployed to perform surveillance and monitoring tasks have to operate under stringent energy and bandwidth limitations. These motivate well distributed estimation scenarios where sensors quantize and transmit only one, or a few bits per observation, for use in forming parameter estimators of interest. We will present interesting tradeoffs that emerge even in distributed mean-location estimation based on severely quantized observations along with their fundamental error-variance limits. Estimators utilizing either independent or colored binary data will be developed and analyzed. Corroborating simulations will provide comparisons with the clairvoyant estimators based on unquantized sensor observations, and include a motivating application with a sensor net employed for habitat monitoring. If time allows, we will also discuss dynamical systems and present (extended) Kalman Filtering ideas based on single-bit observations.

G. B. Giannakis received his B.Sc. in 1981 from the Ntl. Tech. Univ. of Athens, Greece and his M.Sc. and Ph.D. in Electrical Engineering in 1983 and 1986 from the Univ. of Southern California. Since 1999 he has been a professor with the Department of Electrical and Computer Engineering at the University of Minnesota, where he now holds an Endowed ADC Chair in Wireless Telecommunications. His general interests span the areas of communications and signal processing, estimation and detection theory -- subjects on which he has published more than 200 journal papers, 350 conference papers, and two edited books. Current research focuses on complex-field and space-time coding, multicarrier, ultra-wide band wireless communication systems, cross-layer designs and wireless sensor networks. He is the (co-) recipient of six best paper awards from the IEEE Signal Processing (SP) and Communications Societies (1992, 1998, 2000, 2001, 2003, 2004) and also received the SP Society's Technical Achievement Award in 2000. He is an IEEE Fellow since 1997 and has served the IEEE in various editorial and organizational posts.