CENS Technical Seminar Series

Bayesian Selection of non-Faulty Sensors

Presentation Slides (pdf)

Invited Speaker: Kevin Ni, EE, UCLA
Date: August 3, 2007
Time: 1:00 PM - 2:00 PM
Venue: 4760 Boelter Hall, UCLA

Abstract

The identification of sensors returning unreliable data is an important task when working with sensor networks. The detection of these unreliable sensors while in the field can cue human involvement in repairing problem sensors. This ensures that meaningful data is collected throughout the entire length of a sensor deployment. We present a detection based method of identifying faulty and non-faulty sensors from a given set of sensors that are expected to behave similarly. We use a Bayesian detection approach to select a subset of sensors which give the best probability of being correct given the data. This gives us a model from which we can determine whether sensors’ readings fall out of a reasonable range for the sensor set. We apply our method to simulated data and actual environmental data collected in the forest.

Biography

I received my B.S. and M.S. in Electrical Engineering in 2004 and 2005 at UCLA. I am currently a PhD student in the EE department working under the advisement of Prof. Greg Pottie. My research focuses on data integrity in sensor networks. The work I am presenting has been presented at the IEEE International Symposium on Information Theory 2007 in June.