NSF Critical Infrastructures Workshop Nov. 17-18, 2006 Kannan Ramchandran University of California...
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Transcript of NSF Critical Infrastructures Workshop Nov. 17-18, 2006 Kannan Ramchandran University of California...
NSF Critical Infrastructures WorkshopNov. 17-18, 2006
Kannan Ramchandran University of California at Berkeley
Current research interests related to workshop theme:
Distributed signal processing and coding for wireless sensor networks: architectures, algorithms, and protocols for massively scalable robust networks
Robust real-time video transmission over multi-camera wireless surveillance networksMulti-user information and communications theory: constructive designs
Peer-to-peer networks: collaborative relaying, networked storage, p2p communicationSecurity: Data-hiding, steganography & compression/encryption: theory and codes
Sampling of challenges in monitoring & control of large-scale distributed systems (e.g. power grid)
Scaling challenges: What are the scaling laws? Role of decentralized processing? Detection of anomalies: statistical modeling of normal vs. abnormal events How to “in-build” robustness into system architecture to account for dynamics of
environment and fragility of low-cost sensor infrastructure? Architectural challenges related to designing the right levels of heterogeneity & co-
ordination for optimal operation. Power and bandwidth constraints: due to sensor battery power and shared wireless
bandwidth. Addressing delay constraints at multiple scales:
need for operation at multiple time-scales of interest, e.g. need to predict, control, repair, and contain damage quickly and efficiently when event-detection triggers, but
allowing for lenient latencies during “background mode” during non-event-triggered durations.
What are the fundamental theories, (decentralized) architectures, (distributed) algorithms and (randomized) protocols needed to realize this?
Strategic Issues Clear need to “understand the problem domain” more clearly. Need to work with
the domain experts: What are the statistical models for the data? What are the sensor noise models? What is the existing infrastructure that we can leverage in terms of communication,
power, computation? Clear need for a multi-disciplinary systems attack that spans not only EE fields
like control, comm. and signal processing (e.g. info theory, coding theory, estimation &
detection theory, and control theory) but also across EE and CS (randomized algorithms, graph theory, p2p networking,
parallel computing,cryptography), economics (game theory), and operations research (resource optimization, scheduling etc.)
Two important system attributes for next-generation systems: Large-scale and robustness: two attributes that first-generation systems have for the
most part not addressed successfully yet.