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Deborah Estrin

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Title: Deborah Estrin


1
Center for Embedded Networked SensingUpdate,
October 2004
  • Deborah Estrin
  • http//cens.ucla.edu/Estrin
  • destrin_at_cs.ucla.edu
  • Work summarized here is largely that of students,
    staff, and other faculty at CENS
  • We gratefully acknowledge the support of our
    sponsors, including the National Science
    Foundation, Intel Corporation, Sun Inc., Crossbow
    Inc., and the participating campuses.

2
Embedded Networked Sensing
  • Micro-sensors, on-board processing, wireless
    interfaces feasible at very small scale--can
    monitor phenomena up close
  • Enables spatially and temporally dense
    environmental monitoring
  • Embedded Networked Sensing will reveal
    previously unobservable phenomena

Contaminant Transport
Ecosystems, Biocomplexity
Marine Microorganisms
Seismic Structure Response
3
IT Challenges Software and Algorithms
  • Technology Research
  • Self configuring systems for autonomy in
    dynamic, irregular environments
  • In Network Collaborative signal processing and
    Event Detection for Scaling in time and space
  • Exploiting System Ecology, Infrastructure,
    Mobility
  • Data fusion for tasking and interpretation
    multi-mode, multi-scale

Technologies
EmStar
TinyOS
NIMS
Target Apps
Seismic detection, analysis arrays, e.g. CENS
Seismic Array
Seismic
Habitat investigation, e.g NIMS (Networked
Info- Mechanical Systems)
4
CENS Research Organization Road Map
5
CENS Science Application System Drivers
  • Biology/Biocomplexity(Hamilton, Rundel)
  • Robust, extensible microclimate monitoring
  • Image and acoustic sensing
  • Infrastructure based mobility
  • Contaminant Transport (Harmon)
  • Three dimensional soil monitoring
  • Error resiliency at node and system level
  • Data assimilation, model development
  • Seismic monitoring(Davis, Wallace)
  • Wide area, high bandwidth wireless arrays
  • Reliable data delivery
  • Time synchronization
  • Marine microorganisms (Caron, Requicha, Sukhatme)
  • Aquatic operation
  • Micro-organism identification

6
Embedded Mote-based Imaging (Cycl o ps)
  • Inference in optical domain
  • CMOS technology Low power ( capture lt 40mA)
  • Cyclops is not imager but rather a sensor
  • Small picture size Target below 256256
  • Example Applications
  • Color estimation Monitor triggering,
    Agriculture, Motion detection, Security
  • Low power, long term image archival phonology
  • Platform
  • Atmega128 8bit RISC PROCESSOR
  • 512 KByte of Flash for local File system
  • 512 KByte RAM Enough room for heavier computation
  • Software and algorithm innovations
  • in-network processing of images for event
    detection
  • Limited resources, but in limited context

Mohammad Rahimi
7
System Ecology Including Mobility
  • Spatially distributed static nodes
  • Allows simultaneous sampling across study volume
    (dense in time, but possibly sparse in space)
  • Limited energy and sampling rate
  • Articulated Nodes
  • Provide greater functionality for sensors,
    communications
  • Nodes with infrastructure-based mobility
    Networked Info-Mechanical Systems (NIMS)
  • Sensor diversity location, type, duration
  • Allows dense sampling across transect (dense
    spatially, but possibly sparse in time)
  • Adaptive provision of resources (sensors, energy,
    communication)
  • Enable adaptive, fidelity-driven, 3-D sampling
    and sample collection

8
ENS Vision will depend uponHeterogeneous systems
and In-network processing
  • Several classes of systems
  • Mote herds Scale
  • Collaborative processing arrays Sampling rate
  • Networked Info-Mechanical Systems Autonomy
  • Achieve longevity/autonomy, scalability,
    functionality with
  • heterogeneous systems
  • in-network processing, triggering, actuation
  • Algorithm/Software challenges
  • Characterizing and adapting to sensing
    uncertainty
  • Achieving error resiliency, integrity
  • Establishing statistical and information-theoretic
    foundations for adaptive sampling, fusion
  • Developing programming abstractions, Common
    services, tools

lifetime/autonomy
Mote Clusters
Infrastructure- based mobility(NIMS)
scale
Collaborative processing arrays (imaging,
acoustics)
sampling rate
9
Application-Driven, not Application-SpecificComm
on system services
Localization Time Synchronization
Calibration
In Network Processing
Programming Model
Routing and Transport
Event Detection
  • Needed Reusable, Modular, Flexible,
    Well-characterized Services/Tools
  • Routing and Reliable transport
  • Time synchronization, Localization, Calibration,
    Energy Harvesting
  • In Network Processing (compression, triggering),
    Tasking, Fault detection
  • Programming abstractions, tools
  • Development, simulation, testing, debugging

10
NEON
NEON will transform ecological research by
enabling studies on major environmental
challenges at regional to continental scales.
Scientists and engineers will use NEON to conduct
real-time ecological studies spanning all levels
of biological organization and temporal and
geographical scales.
  • Biogeochemical cycles
  • Biodiversity ecosystem functioning
  • Climate change
  • Freshwater resources
  • (especially linkage to land)
  • Infectious diseases
  • Land use change
  • Land use change and
  • Material flux or processing

11
CLEANER California regional effort
  • A multiscale approach - San Joaquin River Basin
    Water quality observation and forecasting--Sierra
    snowpack to San Franciso Bay
  • Harmon, Estrin, Kaiser, Traina, Bales
  • Academics UC Merced, UCLA, UCD, UCR, Caltech
  • Govt Agencies LLNL, LBNL, USBR, USGS, NPS, CA DWR

12
Key Accomplishments
  • Internal Organization
  • Diversity and Education area growth
  • UC Merced partnership (Harmon)
  • NIMS Project
  • Education
  • Very successful undergraduate summer research
    program
  • 7-12 inquiry pilot testing
  • Gender-Diversity program
  • Multi-disciplinary research objectives
  • Cross-disciplinary teams deploying real
    systems--Impossible without STC infrastructure
  • Investigation of fundamental questions across our
    domains
  • New areas of investigation
  • Statistics, Data fusion (Hansen)
  • Programming languages (Kohler)
  • ELSI-ipercs effort (Cuff)
  • Technology development
  • Emstar continued maturity
  • Stargate platform support
  • Nitrate Sensor, LC development
  • NIMS Lab system
  • Testbed deployment
  • NIMS prototypeWind River and JR
  • Factor building data capture
  • JR CMS, Phenology, ESS
  • Contaminant deployment--Palmdale
  • Marine lab facility
  • Marine field experiments-3-mike
  • Community/External visibility
  • Co-Founded and hosted ACM Sensys 2004
  • Co-Founded ACM Transactions on Sensor Networks
  • Hosting IPSN 2005
  • Soils workshop, JR Spring 2004
  • Active in NEON, CLEANER planning
  • Advisory to NSF CISE, ENG, ERE, and NRC panels
  • Pottie-Kaiser, Cambridge Univ Press, Spring 2005

13
  • Principles of Embedded Networked Systems Design
  • Gregory J. Pottie and William J. Kaiser
  • Electrical Engineering Department
  • University of California, Los Angeles

Cambridge University Press Spring,
2005 Preprints in use at Yale, UMass Amherst,
UCLA EE, UCLA CS
14
Undergraduate Research Program
The CENS program went beyond the expected gave
me more learning experiences than anticipated.
Undergraduate Scholar
Originally, I really only considered a masters
program, but seeing the doctoral program and
hearing about it from lab members definitely
generated greater interest on my part to engage
in a similar atmosphere in the future.Undergradu
ate Scholar If mentoring an intern makes up
their mind to attend graduate school or increase
their interest, then mentoring is worth
it.Graduate Student Mentor
  • 45 undergraduates participated in CENS research
    this summer
  • 26 were women
  • 26 were minorities
  • Awarded NSF Gender Diversity
  • in
  • STEM Education grant

15
Broad Relevance to Global Issues
Security
Theatre, Film, Television
Precision Agriculture
Global Climate Change
Public Health
Water Quality
Coral Reef
Global Seismic Grids/Facilities
Early Warning, Crisis Response
Programming
Adaptive Sampling
NIMS
Embedded Imaging
Tools
High Integrity Systems
16
Strong Institutional Support
  • Generous Matching funds from VCR and HS-SEAS
  • Active encouragement and support of
    multi-disciplinary, campus-wide activities
  • HS-SEAS loan for building shell (6000 square
    feet)
  • Currently seeking donor for shell and
    furnishings
  • Excellent naming opportunity

New CENS Building Spring 2005
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