Deborah Estrin

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Center for Embedded Networked Sensing

• Deborah Estrin
• http//cens.ucla.edu/Estrin
• destrin_at_cs.ucla.edu
• Dave Caron
• Dcaron_at_usc.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.

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Who we are Participating Institutions
UCLA
Management Director Deborah Estrin Chief Amin
Officer Bernie Dempsey Education Coor Sara
Terheggen Budget Analyst David Jaquez Admin
Asst Stacy Robinson
Computer Science D. Estrin (PI), R. Guy, E.
Kohler, T. Millstein, R. Muntz, J. Palsberg, S.
Soatto Electrical Engineering J. Judy, W.
Kaiser, G. Pottie, M. Srivastava, K.
Yao Mechanical and Aerospace Engineering C.M.
Ho Civil and Environmental Engineering J.
Wallace Physiological Sciences and Biology P.
Rundel, C. Taylor Earth and Space Sciences P.
Davis (PI), M. Kohler Education and Information
C. Borgman (PI), W. Sandoval Theatre Film and
Television Jeff Burke Architecture Dana Cuff
UC Merced Environmental Engineering T. Harmon
Grades 6-12 The Buckley School K Griffis New
Roads School J. Wise
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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
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CENS Science Application System Developments

• Biology/Biocomplexity(Hamilton, Rundel)
• Microclimate monitoring
• NIMS Adaptive sampling
• Contaminant Transport (Harmon)
• County of Los Angeles Sanitation Districts
  (CLASD) wastewater recycling project, Palmdale,
  CA
• Seismic monitoring(Davis, Wallace)
• 50 node ad hoc, wireless, multi-hop seismic
  network
• Structure response in USGS-instrumented Factor
  Building
• Marine microorganisms (Caron, Requicha, Sukhatme)
• Detection of a harmful alga
• Experimental testbed w/autonously adapting sensor
  location

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ENS IT Challenges
Heterogeneous Technologies

• Technology Research
• Self configuring systems
• Collaborative signal processing
• Networked Info Mechanical Systems
• Embeddable Micro-Sensors
• Embedded Cyber-Infrastructure

EmStar
TinyOS
NIMS
Target Apps
Seismic detection, analysis arrays, e.g. CENS
Seismic Array
Seismic
Habitat investigation, e.g NIMS (Networked
Info- Mechanical Systems)
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Technology Design Themes

• Leverage data processing and heterogeneous
  capabilities in the networked system
• Exploit computation near data to reduce real-time
  communication, achieve scalability even if you
  eventually retrieve all the raw data
• Heterogeneous system elements and capabilities
• Collaborative signal processing, data fusion, and
  triggering
• Flexible tasking incorporating models, analysis,
  fusion with other data sources
• The network is the sensor (MangesSmith, ONL,
  10/98)

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Heterogeneous systems and In-network processing

• Several classes of systems
• Mote herds
• Collaborative processing arrays
• Networked Info-Mechanical Systems
• In-network triggering, processing, actuation to
  enable
• Longevity, Autonomy, Scalability
• Characterizing sensing uncertainty
• Error resiliency, integrity
• CENS IT Activities Develop algorithms, tools,
  methods
• Programming abstractions
• Emstar system
• Statistical and information-theoretic foundations

lifetime/autonomy
Mote herds
Infrastructure- based mobility(NIMS)
scale
Collaborative processing arrays (imaging,
acoustics)
datarate
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Emerging technology System Ecology

• Spatially distributed static nodes
• Allows simultaneous sampling across study volume
  (dense in time, but possibly sparse in space)
• Limited energy
• Interesting capabilities in the future such as
  application specific imaging (cyclopsAgilent)
• Articulated Nodes
• Provide greater functionality for sensors,
  communications
• Nodes with infrastructure-based mobility NIMS
• Sensor diversity location, type, duration
• Allows dense sampling across transect (dense
  spatially, but possibly sparse in time)
• Adaptive provision of resources (sensors, energy,
  comm.)
• Enable adaptive, fidelity-driven, 3-D sampling

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Cyclops

• 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

Mohammad Rahimi
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Need Common system services Embedded
Cyberinfrastructure
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 Storage, Processing, Triggering,
  Tasking
• Programming abstractions, tools
• Development, simulation, testing, debugging

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Emstar Development and Deployment Software

• Software system designed to support heterogeneous
  ENS applications
• 32-bit Linux, and 8-bit TOS, devices
• Robustness and Transparency
• Finely decomposed, modularized software
• Tool chest of modules neighbors, link
  estimation, timesync, routing, reliable state
  synchronization...
• Provide rich forms of inter-module communication
• Run-time environments for deep debugging
• Debug in a transparent context before the
  necessarily opaque deployment
• Same code runs in simulation, reality, and
  hybrids
• High visibility -- status is exposed in both
  human- and machine-readable form

Animal Call Localizer
collab_detect
gradients
data
MicroDiffusion
sensor/frog
detect
link/ls0/neighbors
neighbors
sensor/audio/fft
sync/hist
FFT
timehist
clients
emproxy
link/ls0
linkstats
sync/params
sensor/audio/0
syncd
audiod
status
802.11
link/udp0
emlog/
udpd
emrun
ADC
802.11 NIC
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Programming Languages for Sensor Network
Applications

• Programming problems
• Codependent components, representing timer
  interactions,
• Use a programming language to solve them
• Goal Smart sensor network service libraries
• System designers build parameterized libraries
• Examples temperature sensing, sensor value
  smoothing, routing tree formation, link quality
  estimation, query processing,
• More flexible application components than
  conventional nesC
• Scientists plug libraries together to build
  applications
• The libraries weave themselves into an efficient
  program

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Towards Embedded Cyber-infrastructure
As systems scale, software and middleware is not
only on the backend

• Embeddable Devices
• Energy-conserving platforms, radios
• Miniaturized, autonomous, sensors
• Standardized software interfaces

• Deployed systems in support of
• engineering and science applications
• Environmental, Civil, Bioengineering
• Bio and Geo Sciences

• NSF-Wide Collaboration
• CISE and Engineering systems, technology
• Other NSF Divisions apply and test systems
  (biological sciences, earth sciences)
• Other agencies and industry extend
  systems(DOE, EPA, DHS, DOD, )

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Pattern-Triggered Data Collection in Marine
environment
Biosensors Correlatiing small-scale spatial
distributions with chemical and physical structure
Sensor network Defining small-scale physical
structure
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Experiment Design

• Create thermocline in tank sensors find it
• Acquire water samples at identified points
• Determine microorganism content

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Addition of mobile nodes

• A tethered system of small robots with radios
  (limited range)
• A small number of mobile underwater robots
• Initially focus on temperature measurements
• Collect water samples for offline analysis

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Temperature profile and growth of Brown Tide alga
with depth in column
Brown Tide Cells/ml
thermocline
0 2x106 4x106
6x106
T1 day
0
T3 day
T7 day
T13 day
50
T15 day
T18 day
T21 day
Depth (cm)
T22 day
100
T27 day
T29 day
150
Addition of BT grazer, Pedinella
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0 5 10 15 20
25 30
Temperature (C)
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Where are we going with these experiments?
Fundamental questions What factors
(environmental or otherwise) explain the
distribution of microrganisms of interest to
ecosystem or human health? What factors
(chemical, physical, biological) lead to the
competitive exclusion of some species and the
success of others? Not simply a question of
more data (although that also helps). CENS
approach will provide the ability to characterize
the chemical, physical, biological community on
temporal and spatial scales that are pertinent to
the organisms.
also, as we move into the real world
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Micro scale distributions of aquatic
microorganisms (What establishes the patterns?
What do the patterns mean?)
Sampling and identifying fine-scale biological
features in real time is presently impossible.
From Donaghy et al. (1992)
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Small scale distributions of aquatic
microorganisms
The distribution of symbiotic algal species in
corals is not uniform
Possible variables light intensity water
flow food acquisition
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Regional scale distributions of aquatic
microorganisms
How to characterize and sample features of
biological interest in aquatic ecosystems?
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Amnesic shellfish poisoning in Southern California
Domoic acid concentrations (ng/liter) in the LA
harbor and San Pedro Channel in May, 2002
Without networked sensors, the result is often
lots of sampling, very little information.
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Sampling at a subset of fixed nodes based on
sensing-based decision
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Aggregation of sampling nodes at feature(s) of
interest based on sensing-directed movement
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Pre-set grid pattern or sensor-actuated, bias
random walk to features of interest.
Sensor-actuated multiple water sampling.
Ultimately, on-board biosensors.
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