Deborah Estrin

1
Center for Embedded Networked SensingOverview
December 2004

• Deborah Estrin
• http//cens.ucla.edu/Estrin destrin_at_cs.ucla.edu
• Work summarized here is that of students, staff,
  and 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
Remote and In Situ Sensing

• Remote sensing has transformed observations of
  large scale phenomena
• In situ sensing will similarly transform
  observations of spatially variable processes in
  heterogeneous and obstructed environments

Red Soil Green Vegetation Blue Snow
SPOT Vegetation Daily Global Coverage SWIR 3 Day
Composite
Predicting Soil Erosion Potential Weekly MODIS
Data
Sheely Farm 2002 Crop map
San Joaquin River Basin Courtesy of Susan
Ustin-Center for Spatial Technologies and Remote
Sensing
4
Environmental Monitoring Applications Exhibit
High Spatial Variations and Heterogeneity
Overflow of embankment
Precision Agriculture, Water quality management
Algal growth as a result of eutrophication
-image courtesy of The J. for Surface Water
Quality Professionals
5
CENS Research Organization Road Map
6
CENS Science Application System Examples

• Biology/Biocomplexity(Hamilton, Rundel)
• Microclimate and image datasets
• First NIMS Prototypes, Deployments
• Contaminant Transport (Harmon)
• County of Los Angeles Sanitation Districts
  wastewater recycling project, Palmdale, CA
• CLEANER planning grant
• Seismic monitoring(Davis, Wallace)
• 50 node ad hoc, wireless, multi-hop seismic
  network for Mexico 2005
• Structure response in 72-channel
  USGS-instrumented Factor Building
• Marine microorganisms (Caron, Requicha, Sukhatme)
• Detection of harmful alga and experimental
  testbed w/autonously adapting sensor locations
• Field tested mobile sample collector
  (robo-medusa)

7
Application Driven System EcologyHeterogeneous
systems and In-network processing

• Several classes of systems
• Mote herds (8 bit, low bw, TOS, low power) Scale
• Collaborative processing arrays (32 bit, 802.11,
  linux) Signal BW
• Networked Info-Mechanical Systems Autonomy
• Achieve longevity/autonomy, scalability,
  performance with
• heterogeneous systems
• in-network processing, triggering, actuation
• Algorithm/Software challenges
• Characterizing sensing uncertainty
• Error resiliency, integrity
• Statistical and information-theoretic foundations
  for adaptive sampling, fusion
• Programming abstractions, Common services, tools

lifetime/autonomy
Mote Clusters
Infrastructure- based mobility(NIMS)
scale
Collaborative processing arrays (imaging,
acoustics)
sampling rate
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Application Driven System EcologyHeterogeneous
systems and In-network processing
1/Latency of detection
Trigger imager
Image histogram
NIMS, Calibration
Number of measurement points needed
Computational/BW Overhead Per Sample
Seismic/Acoustic inverse problems

• We can also classify requirements for in network
  processing and actuation based on
• Latency requirements
• Number of measurement points needed
• Computational and bandwidth overhead per sample

9
Wastewater reuse in the Mojave Desert
Reclaimed wastewater irrigation pivot plots

• Where does the County Sanitation District (CSD)
  of Los Angeles put 4 million gallons per day of
  treated wastewater in a landlocked region?
• Stakeholders
• County Sanitation District
• Farmer
• Water Quality Board

Palmdale, CA wastewater treatment plant
10
Locally dense surface and subsurface sensor
networks

• Modular clustered sensing targeting specific
  questions
• What is quantitative flux of nitrate past the
  plants root zone?
• What are the spatiotemporal variations associated
  with nitrogen biogeochemical cycling in the soil?
• How does the network optimally feedback toward
  sustainable fertilizer application?
• Spatial granularity 10s of meters to cm...
• Remote sensing, stationary and mobile nodes
  (e.g., distributed soil pylons, autonomous
  tractor-mounted sensors, aerial NIMS devices)
• Data interpolation, network calibration, and
  forecasting using detailed computational models

Nitrate sensor mimicking plant root fibers
Geostatistical realization of soil properties
Courtesy of Tom Harmon
11
Plankton dynamics in marine environments
Spatial and temporal distributions of harmful
alga blooms (red, green, brown tides) in marine
coastal ecosystems
Experimental and observational studies of
chemical, physical and biolgical features
promoting bloom events
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Important Challenges for ENS Applications

• Robust, portable, self configuring systems

• Embeddable sensor devices for specific species,
  sensitivity, longevity

• Data Integrity, Calibration

• Multiscale Data Fusion

13
Systems Challenges

• InformationTechnology 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 Heterogeneous Systems w/ Mobility
• Multi-mode, multi-scale data fusion for tasking,
  interpretation,

Key Constraints
Low-Power Platforms
Energy awareness and conservation
Scaling and adaptation to variable resourcesand
stimuli
Software, Protocols
Embeddable Sensors
Autonomous, disconnected operation
NIMS
Target Apps
Data Integrity given sensing channel uncertainty
Complexity of Distributed systems
Seismic
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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
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Environmental Focus for Sensor Group

• Initial emphasis or chemical species (ionic)
• specifically nitrate
• Activities
• nitrate ISE, demonstrate scalability
• higher performance amperometric nitrate sensor
• general ion separation/identification
  capabilities (IC-on-a-chip)
• proof-of-concept for a more general chemical
  sensor based on surface plasma resonance
• transitioning to gas/atmospheric project
  infrared CO2 sensing

16
Sensor Arrays for Acoustic Monitoring of Bird
Behavior and Diversity

• Identify and locate inter-specie and intra-
  specie of birds
• Use acoustical array to perform SNR
  enhancement for identification and localization
• Trigger imagers and human observers with
  solar-powered or short-term deployments
• Direction-of-arrival (DOA) algorithm used to
  calculate bearing crossings to locate bird(s)
• Acoustic array based on Stargates, 802.11,
  Emstar software
• Near-optimal Approximate Maximum-Likelihood
  based algorithm

17
System Ecology Includes 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

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Data Integrity How will we monitor the monitors?
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Data integrity in sensor networks multilevel
calibration

• Bench-top calibration
• Pilot deployment
• develop in situ calibration protocol
• characterize longevity, degradation
• Early in the deployment
• Take advantage of the sensors integrity
• Calibrate model (distributed parameters)
• Integrate DAQ with simulator to accelerate
  process
• Later (as sensors become suspect)
• Reverse the process
• Let the network identity bad sensors Self-Test
• Incorporate uncertainty into the process

20
Multiscale Observation and Fusion Example,
Regional (or greater) scale to local scale

• Satellite, airborne remote sensing data sets at
  regular time intervals
• coupled to regional-scale backbone sensor
  network for ground-based observations
• fusion, interpolation tools based on large-scale
  computational models

Example identification of invasive riparian
species using HyMap (airborne hyperspectral
scanning)
images from Susan Ustin, UC Davis
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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 Triggering, Tasking, Fault
  detection, Sample Collection
• Programming abstractions, tools
• Development, simulation, testing, debugging

22
EmStar development environment

• EmStar is a layer above Linux designed to enable
• Robustness Keep running despite unexpected
  failures and bugs
• Visibility Easily debug/diagnose running systems
• Simulation, Emulation Rapid iteration via
  real-code simulation tools
• Module Reuse Leverage existing libraries, tools,
  and services
• EmTos Wrapper library provides TinyOS API and
  Services

Robust multi-process, microkernel architecture
Simulation Framework with real RF channels
Visualization Tools
23
Towards Embedded Cyber-infrastructure

• 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

• Collaboration
• NSF CISE and Engineering systems, technology
• NSF Science Directorates apply and test
  systems (Bio, Geo, Env Engineering)
• Other agencies and industry extend
  systems(EPA, FDA, DOE, DHS, DOD, )

24
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

25
California regional effort

• A multiscale approach - San Joaquin River Basin
  Water quality observation and forecasting--Sierra
  snowpack to San Franciso Bay
• Academics UC Merced, UCLA, UCD, UCR, Caltech
• Govt Agencies LLNL, LBNL, USBR, USGS, NPS, CA DWR

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Key Accomplishments, 2004

• 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
• Sensor OS (SOS) devel and use
• Testbed deployment
• NIMS prototypeWind River and JR
• Factor building data capture
• JR CMS, Phenology, ESS2 tests
• 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

27
Key Plans, 2005

• Education
• Tech camp June 2005
• Increased diversity recruitment to graduate
  program
• Expanded 7-12 inquiry pilot testing
• Physical Science inquiry development
• Exploration of LA Unified campus sensor
  deployments

• 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)
• Augmented Reality interface

• Technology development
• Emstar support, and tutorials
• Stargate and imote2 platform alpha users and
  community support
• Nitrate Sensor, LC development
• NIMS Lab systems
• Sensor OS (SOS) devel and use
• Cyclops development and support
• Testbed deployment
• NIMS2 at JR and Costa Rica LS
• Mexico seismic array deployment
• Factor building data analysis
• JR CMS, Nest Boxes and ESS2
• Contaminant deployment--Palmdale
• Marine field experiments-3-mike

• Community/External visibility
• Steering committee for SenSys 05
• Hosting IPSN 2005
• Other conferences DCOSS 2005, EmNets 2005
• Planned workshops Acoustics, AR
• Active in NEON, CLEANER activities
• Advisory to NSF CISE, ENG, ERE, and NRC panels
  continues
• Pottie-Kaiser, Cambridge Univ Press, Spring 2005

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Diversity, Education, Ethics
Undergraduate Research _at_ CENS
Diversity _at_ CENS
7-12-Education _at_ CENS
Ethics _at_ CENS
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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
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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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For Further Investigation

• Center for Embedded Networked Sensing,
  http//cens.ucla.edu
• TInyOS and Mote platforms UC Berkeley, Intel,
  Crossbow, Sensicast, Dust Networks, Ember
• NSF Workshops including Sensors for Environmental
  Observatories, http//www.wtec.org/seo/seo6.htm
• National Ecological Observatory Network,
  http//neoninc.org
• Principles of Embedded Networked Systems Design,
  Gregory J. Pottie and William J. Kaiser,
  Cambridge University Press, Spring 2005