Date: Nov 6th, 2008
Room: DMP 310
Speaker: Carlos Guestrin
Title: Optimizing Sensing from Water to the Web

Abstract:

In this talk, we tackle a fundamental problem that arises when using sensors to monitor the ecological condition of rivers and lakes, the network of pipes that bring water to our taps, or the activities of an elderly individual when sitting on a chair: Where should we place the sensors in order to make effective and robust predictions?

Such sensing problems are typically NP-hard, and in the past, heuristics without theoretical guarantees about the solution quality have often been used. In this talk, I will present algorithms which efficiently find provably near-optimal solutions to large, complex sensing problems. Our algorithms are based on the key insight, that many important sensing problems exhibit submodularity, an intuitive diminishing returns property: Adding a sensor helps more if we have placed few sensors so far, and less if we have already placed many sensors. In addition to identifying most informative locations for placing sensors, our algorithms can handle settings, where sensor nodes need to be able to reliably communicate over lossy links, where mobile robots are used for collecting data or where solutions need to be robust against adversaries and sensor failures.

I will present results applying our algorithms to several real-world sensing tasks, including environmental monitoring using robotic sensors, activity recognition using a built sensing chair, and a sensor placement competition. I will conclude with drawing an interesting connection between sensor placement for water monitoring, and the problem of selecting blogs to read in order to learn about the biggest stories discussed on the web.

This talk is primarily based on joint work with Andreas Krause.