Opportunistic Routing in Wireless Networks: A Stochastic/Adaptive Control Approach
Date, Time, and Place: Monday, Sept. 22, 2008 from 3:45-4:45 pm in 1106 ME.
Opportunistic routing for multi-hop wireless networks has seen recent
research interest to overcome the deficiencies of traditional routing.
Opportunistic routing mitigates the impact of poor wireless links by
exploiting the broadcast nature of wireless transmissions as well as the
existing path and multi-user diversities. Specifically, the routing
decisions are made opportunistically, choosing the next relay based on the
actual transmission outcomes in addition to an expected sense of future
costs. The performance improvements associated with opportunism are achieved
when statistical behavior of the channels are well modeled by a conditional
probability distribution specifying the statistics of the channels
across the network. In the first part of the talk, we, briefly, review the
fundamentals of opportunistic routing as a Markov decision problem (MDP); in
particular, we revisit the convergence and optimality of a known set of
centralized and distributed routing schemes given a error-free and
time-invariant probabilistic model of local (broadcast) transmissions.
A perfect probabilistic model of channel qualities and network topology is
restrictive in a practical network setting. This motivates a sensitivity
analysis in which the performance of the proposed opportunistic
algorithms are investigated in presence of an erroneous model. Using this
framework, in the second part of the talk, the robustness of the proposed
algorithms are discussed and presented. In the last part of the talk, by
contrast, we investigate the problem of opportunistically routing packets in
a wireless multi-hop network when zero or little knowledge about
transmission success probabilities can be assumed. Using an on-line learning
framework, we propose an adaptive opportunistic routing algorithm which
minimizes the expected cost per packet independently of the initial
knowledge about the channel quality and statistics across the network.
Tara Javidi studied electrical engineering at Sharif University of Technology, Tehran, Iran from 1992 to 1996. She received the MS degrees in electrical engineering (systems), and in applied mathematics (stochastics) from the University of Michigan, Ann Arbor. She received her Ph.D. in electrical engineering and computer science from the University of Michigan, Ann Arbor, in May 2002.
From 2002 to 2004, she was an assistant professor of electrical engineering at the University of Washington, Seattle. She joined University of California, San Diego, in 2005, where she is currently an assistant professor of electrical and computer engineering. She was a Barbour Scholar during 1999-2000 academic year and received an NSF CAREER Award in 2004. Her research interests are in communication networks, stochastic resource allocation, and wireless communications.
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