|Speaker||Dr. Maxim Raginsky|
|Date||August 31, 2007 12:30 PM|
Consider a scenario where a large number of sensors are deployed at random over a given geographical region, where they will sense some phenomenon of interest, such as the concentration of a poisonous chemical or pressure of a gas. They will then transmit their measurements via a wireless channel to a fusion center, whose task is to fit a function of location to the sensor observations in the least-squares sense in order to acquire a good model of the phenomenon being sensed. Such a network will typically be on a tight power budget, so the sensors will have to compress their observations before passing them on to the fusion center. I will present a novel scheme which is (a) low-complexity, requiring the sensors to carry out simple operations such as uniform scalar quantization with additive dither, as well as simple message passing, and (b) universal, in the sense that it is applicable to measurements, provided some mild regularity conditions are met. The scheme comes with solid theoretical guarantees on the average number of bits sent out by the network and on the mean-squared error incurred by the fusion center in estimating the regression function. I will also present results of numerical simulations, which validate the theory.
Maxim Raginsky, received the B.S. and M.S. degrees in 2000 and the Ph.D. in 2002 from Northwestern University,