Seminars & Colloquia
Computer Science Dept., Duke University
"Suppression Methods for Data Collection in Sensor Networks"
Thursday April 26, 2007 09:30 AM
Location: 3211, Engineering Building II NCSU Centennial Campus
(Visitor parking instructions)
I will present model-encoding suppression schemes for producing continuous query results without continuous reporting. The most effective models incorporate both temporal and spatial correlations. While building schemes to leverage either of these types is straightforward, combining them is not. I will discuss a scheme, Conch, which effectively couples the correlations by temporally monitoring spatial constraints, to minimize transmission cost.
Sensor networks are prone to transmission failures, which are especially detrimental to suppression schemes; a suppressed report cannot be distinguished from a failed one. The cost of implementing reliable transmission in the communication layer is prohibitively high. Instead, I show how to make schemes robust with application-level redundancy in monitoring and reporting. I discuss BaySail, a framework that uses Bayesian analysis to recover missing data. In inference, it augments data received from the network with knowledge of the suppression scheme and the spatio-temporal correlations to highly constrain the uncertainty caused by missing data.
Host: Rada Chirkova, Computer Science Dept., NCSU