[Statseminars] Seminar
Amy Mulholland
amy.mulholland at yale.edu
Tue Oct 31 08:31:03 EST 2006
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>To: Amy Mulholland <amy.mulholland at yale.edu>, huibin.zhou at yale.edu
>From: Sekhar Tatikonda <sekhar.tatikonda at yale.edu>
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>Hi Amy, Harry,
>The following talk may be of interest to the Stat's community.
>Regards,
>Sekhar
>
>----------------------------------
>
>
>Yale Communications and Networking Seminar
>Yale University
>Wednesday, November 1, 2006
>4:00 p.m., Watson 500
>
>Connectivity, Devolution, and Lacunae in Geometric Random Graphs
>Or, How a Sensor Network Loses Its Groove
>
>Santosh Venkatesh
>University of Pennsylvania
>
>Abstract: A mosaic process is formed by placing a small random set at each
>point in a collection of a large number of random points in the unit disc.
> From each point we draw a directed edge outward to every other point in
>the collection that lies within its random set. The mosaic thus induces a
>geometric random digraph on the collection of points. This setting can
>model a network of randomly placed sensors, each equipped with a limited,
>possibly anisotropic, communication capability. If, following
>establishment, vertices are extinguished at random times, we obtain a
>sanitized model of network devolution. Here vertex extinctions model node
>failures due to battery exhaustion. If the random sets forming the mosaic
>are sufficiently regular then, initially, a threshold function for graph
>connectivity is manifested at a critical rate of coverage by the small
>sets. Following establishment, as vertices are extinguished, the
>devolution of the graph with time is characterized by a succession of
>phase transitions at which isolated vertices appear, followed by the
>sudden appearance of sensory lacunae or dead spots, and, eventually, an
>abrupt breakdown in connectivity between survivors. I will show that these
>results can be explicated using elementary arguments in the classical
>Poisson paradigm. These results are from joint work with Srisankar S.
>Kunniyur.
>
>Biography: Santosh Venkatesh received the B.Tech. degree with distinction
>from the Indian Institute of Technology, Bombay, India in 1981, and the
>M.S. and Ph.D. degrees from the California Institute of Technology,
>Pasadena, California in 1982 and 1986, respectively, all in electrical
>engineering. Since 1986 he has been on the faculty of the Electrical and
>Systems Engineering Department at the University of Pennsylvania,
>Philadelphia where he is also a member of the David Mahoney Institute for
>Neurological Sciences. Dr. Venkatesh's research interests include applied
>probability, network information theory, cyber security, pattern
>recognition, computational learning theory, and neural networks.
>
>
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