Abstract: The distribution of networked sensors in the environment offers exciting new possibilities for sensing and monitoring. Sensors can be placed close to multiple signal sources so that, collaboratively, they can sense and reason about wide-area phenomena, while providing a distributed awareness that no centralized system can attain. To bring this vision to fruition, however, several technological challenges remain. One of these is the design of lightweight distributed algorithms and protocols that enable the network to self-organize and then aggregate local information as needed, in order to reach global conclusions. This must be done in a scalable and robust way to allow systems with a large and variable number of relatively frail nodes that are able to deal with uncertainty and incomplete information. In this talk we discuss a number of algorithmic paradigms that address these issues, at least in part. We present examples such as tracking wide-area phenomena, counting people or vehicles, and performing identity management.

Short Bio: Leonidas Guibas obtained his Ph.D. from Stanford in 1976, under the supervision of Donald Knuth. His main subsequent employers were Xerox PARC, MIT, and DEC/SRC. He has been at Stanford since 1984 as Professor of Computer Science. He has produced several Ph.D. students who are well-known in computational geometry, such as John Hershberger, Jack Snoeyink, and Jorge Stolfi, or in computer graphics, such as David Salesin and Eric Veach. At Stanford he has developed new courses in algorithms and data structures, geometric modeling, geometric algorithms, and sensor networks. Professor Guibas is an ACM Fellow.