Smart Networks of Dense Nodes using Distributed Signal Processing and Coding

Kannan Ramchandran
University of California, Berkeley

Monday, November 26, 2007 at 1:00PM

54-134 Engineering IV Building
Refreshments Served

Abstract: As wireless sensor networks continue to profoundly impact the way in which we interact with the physical world, we are increasingly being faced with the challenge of scaling the system densely, seamlessly, and robustly. The need for scalability naturally imposes constraints on individual node resources and reliability. Yet, as a collection, can these nodes be made to overcome their individual deficiencies, deriving strength from numbers, and realizing a network that is strong, robust, and capable of reliably meeting quantifiable performance guarantees without centralized intelligence or global co-ordination which are more susceptible to single points of failure? We will explore this vision and highlight how a minimalist, randomized and distributed approach to signal processing and coding can help tackle the two important system attributes of scale and robustness. We will provide, time permitting, concrete illustrations of this paradigm to several scenarios including (i) reliable multi-hop communication in dense ad hoc networks (ii) decentralized networked and peer-to-peer storage, and (iii) distributed multi-resolution representation in large-scale networks.

Biography: Kannan Ramchandran (Ph.D. 1993, 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 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.