## Distributed Computation in
Sensor Networks via Gossip : Faster Algorithms and a Signal Processing
Application

### Michael Rabbat

*Department of Electrical and Computer
Engineering*

McGill Unviersity

###
Friday, April 9, 2pm

Location: BAB024 (Bahen Centre Basement)

### Abstract:

Gossip algorithms are an
attractive paradigm for distributed computation in sensor networks for a
number of reasons including: they run asynchronously, they do not require
overhead to establish or maintain routing tables, and they are not susceptible
to bottlenecks or single points of failure. Nodes iteratively exchange
information with their immediate neighbors until the entire network reaches a
consensus on the value being computed. Of course, robustness and autonomy come
at a price. For network topologies typically used to model connectivity in
wireless networks, such as grids and random geometric graphs, the average
number of messages transmitted by each node grows linearly with the size of
the network, and hence does not scale. In wireless sensor networks this is
especially detrimental since each transmission consumes valuable bandwidth and
energy resources. In this talk I will describe recent work on speeding up
gossip algorithms by incorporating a constant amount (just 2 taps) of memory
at each node. For grids and random geometric graphs, this reduces the number
of messages per node from scaling linear to scaling like the square root of
the number of nodes in the network. I will also present an example of how a
variant of gossip can be used in a decentralized compression application,
where one wishes to adaptively compute the most significant (largest energy)
coefficients of a linear transform of distributed data without knowing their
indices in advance. These results are based on joint work with Rui Castro,
Mark Coates, Boris Oreshkin, and Deniz Ustebay.

### Bio:

Michael
Rabbat earned the B.Sc. from the University of Illinois at Urbana-Champaign
(2001), the M.Sc. degree from Rice University (2003), and the Ph.D. from the
University of Wisconsin–Madison (2006), all in electrical engineering. He is
currently an Assistant Professor at McGill University. He was a visiting
researcher at Applied Signal Technology, Inc., during the summer of 2003. He
received the Best Student Paper award at the 2004 ACM/IEEE Conference on
Information Processing in Sensor Networks, Outstanding Student Paper Honorable
Mention at the 2006 Conference on Neural Information Processing Systems, and the
Harold A. Peterson Thesis Prize. His current research focuses on distributed
information processing in sensor networks, network monitoring, and network
inference.

### Host of Talk:

Jörg Liebeherr (jorg@comm.toronto.edu)