Making Dense Networks Smarter with Randomized Network Coding and Distributed Signal Processing

By

Prof. Kannan Ramchandran
Professor, Department of Electrical Engineering and Computer Science, University of California at Berkeley

Date: Nov 26, 2008 (Wednesday)

Time: 2:30pm - 3:30pm

Venue: Rm. 1009, William M.W. Mong Engineering Building, CUHK

Abstract :

The proliferation of wireless sensor and ad hoc networks has highlighted the systems challenge of robustly scaling these systems. The need for robust scalability naturally imposes constraints on individual node resources and reliability. Yet, as a collection, can these nodes be made to overcome their individual limitations in realizing a quantifiably efficient network by deriving strength from numbers? Can this further be done with little or no centralized control or global co-ordination? We will explore this vision in some concrete large-scale network settings by emphasizing a minimalistic, distributed and randomized approach to network coding and signal processing. The scenarios we explore, time permitting, include: (i) decentralized networked and peer-to-peer storage; (ii) reliable multi-hop communication; and (iii) distributed multi-resolution representation.

Biography :

Kannan Ramchandran (Ph.D.93, Columbia University) is a Professor of Electrical Engineering and Computer Science at the University of California at Berkeley, where he has been since 1999. Prior to that, he was with the University of Illinois at Urbana-Champaign from 1993 to 1999, and was at AT&T Bell Laboratories from 1984 to 1990. His current research interests include distributed signal processing algorithms for wireless sensor and ad hoc networks, multimedia and peer-to-peer networking, multi-user information and communication theory, and wavelets and multi-resolution signal and image processing. Prof. Ramchandran is a Fellow of the IEEE. His research awards include the Elaihu Jury award for the best doctoral thesis in the systems area at Columbia University, the NSF CAREER award, the ONR and ARO Young Investigator Awards, two Best Paper awards from the IEEE Signal Processing Society, a Hank Magnuski Scholar award for excellence in junior faculty at the University of Illinois, and an Okawa Foundation Prize for excellence in research at Berkeley. He is a Fellow of the IEEE. He has published extensively in his field, holds 8 patents, serves as an active consultant to industry, and has held various editorial and Technical Program Committee positions.